1 Changelog

  • 202401-202405: Cleanups, formatting, ensuring that everything works in the container.
  • 202310: Cleaning up to make everything pass within a containerized environment.
  • 202310: Received a set of colors and contrasts of interest for a barplot of significance.
  • 20230410: Making some changes to improve the differential expression plots as well as prepare for some different pathway/GSEA/GSVA analyses on the data.

2 Introduction

Having established that the TMRC2 macrophage data looks robust and illustrative of a couple of interesting questions, let us perform a couple of differential analyses of it.

Also note that as of 202212, we received a new set of samples which now include some which are a completely different cell type, U937. As their ATCC page states, they are malignant cells taken from the pleural effusion of a 37 year old white male with histiocytic lymphoma and which exhibit the morphology of monocytes. Thus, this document now includes some comparisons of the cell types as well as the various macrophage donors (given that there are now more donors too).

2.1 Human data

I am moving the dataset manipulations here so that I can look at them all together before running the various DE analyses.

2.2 Create sets focused on drug, celltype, strain, and combinations

Let us start by playing with the metadata a little and create sets with the condition set to:

  • Drug treatment
  • Cell type (macrophage or U937)
  • Donor
  • Infection Strain
  • Some useful combinations thereof

In addition, keep mental track of which datasets are comprised of all samples vs. those which are only macrophage vs. those which are only U937. (Thus, the usage of all_human vs. hs_macr vs. u937 as prefixes for the data structures.)

Ideally, these recreations of the data should perhaps be in the datastructures worksheet.

all_human <- sanitize_expt_pData(hs_macrophage, columns = "drug") %>%
  set_conditions(fact = "drug", colors = color_choices[["drug"]]) %>%
  set_batches(fact = "typeofcells")
## Error in set_batches(., fact = "typeofcells"): could not find function "set_batches"
## The following 3 lines were copy/pasted to datastructures and should be removed soon.
no_strain_idx <- colData(all_human)[["strainid"]] == "none"
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'all_human' not found
##pData(all_human)[["strainid"]] <- paste0("s", pData(all_human)[["strainid"]],
##                                         "_", pData(all_human)[["macrophagezymodeme"]])
colData(all_human)[no_strain_idx, "strainid"] <- "none"
## Error: object 'all_human' not found
table(colData(all_human)[["strainid"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'all_human' not found
all_human_types <- set_conditions(all_human, fact = "typeofcells") %>%
  set_batches(fact = "drug")
## Error in set_batches(., fact = "drug"): could not find function "set_batches"
type_zymo_fact <- paste0(colData(all_human_types)[["condition"]], "_",
                         colData(all_human_types)[["macrophagezymodeme"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'all_human_types' not found
type_zymo <- set_conditions(all_human_types, fact = type_zymo_fact)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'all_human_types' not found
type_drug_fact <- paste0(colData(all_human_types)[["condition"]], "_",
                         colData(all_human_types)[["drug"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'all_human_types' not found
type_drug <- set_conditions(all_human_types, fact = type_drug_fact)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'all_human_types' not found
strain_fact <- colData(all_human_types)[["strainid"]]
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'all_human_types' not found
table(strain_fact)
## Error: object 'strain_fact' not found
new_conditions <- paste0(colData(hs_macrophage)[["macrophagetreatment"]], "_",
                         colData(hs_macrophage)[["macrophagezymodeme"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'hs_macrophage' not found
## Note the sanitize() call is redundant with the addition of sanitize() in the
## datastructures file, but I don't want to wait to rerun that.
hs_macr <- set_conditions(hs_macrophage, fact = new_conditions) %>%
  sanitize_expt_pData(column = "drug") %>%
  subset_expt(subset = "typeofcells!='U937'") %>%
  set_se_colors(color_choices[["treatment_zymo"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': error in evaluating the argument 'object' in selecting a method for function 'pData': error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'hs_macrophage' not found

2.2.1 Separate Macrophage samples

Once again, we should reconsider where the following block is placed, but these datastructures are likely to be used in many of the following analyses.

hs_macr_drug_expt <- set_conditions(hs_macr, fact = "drug", colors = color_choices[["drug"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'hs_macr' not found
hs_macr_strain_expt <- set_conditions(hs_macr, fact = "macrophagezymodeme",
                                      colors = color_choices[["zymo"]]) %>%
  subset_se(subset = "macrophagezymodeme != 'none'")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'hs_macr' not found
table(colData(hs_macr)[["strainid"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'hs_macr' not found

2.2.2 Refactor U937 samples

The U937 samples were separated in the datastructures file, but we want to use the combination of drug/zymodeme with them pretty much exclusively.

new_conditions <- paste0(colData(hs_u937)[["macrophagetreatment"]], "_",
                         colData(hs_u937)[["macrophagezymodeme"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'colData': object 'hs_u937' not found
u937_expt <- set_conditions(hs_u937, fact = new_conditions,
                            colors = color_choices[["treatment_zymo"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'se' in selecting a method for function 'set_conditions': object 'hs_u937' not found

2.3 Contrasts used in this document

Given the various ways we have chopped up this dataset, there are a few general types of contrasts we will perform, which will then be combined into greater complexity:

  • drug treatment: Antimonal treated or not.
  • strains used: Uninfected, z2.3, and z2.2.
  • cellltypes: U937 or macrophage.
  • donors: The person from whom the macrophages were taken.

In the end, our actual goal is to consider the variable effects of drug+strain and see if we can discern patterns which lead to better or worse drug treatment outcome.

There is a set of contrasts in which we are primarily interested in this data, these follow. I created one ratio of ratios contrast which I think has the potential to ask our biggest question.

## Each of the following lists has the name of the contrast as the key
## followed by a two element vector comprised of the numerator and
## denominator as the value.  In the case of this first contrast, that
## is comprised of a string which manually defines a series of more
## complex contrasts than the usual/simple pairwise.
tmrc2_human_extra <- "z23drugnodrug_vs_z22drugnodrug = (conditioninf_sb_z23 - conditioninf_z23) - (conditioninf_sb_z22 - conditioninf_z22), z23z22drug_vs_z23z22nodrug = (conditioninf_sb_z23 - conditioninf_sb_z22) - (conditioninf_z23 - conditioninf_z22)"
tmrc2_human_keepers <- list(
  "z23nosb_vs_uninf" = c("inf_z23", "uninf_none"),
  "z22nosb_vs_uninf" = c("inf_z22", "uninf_none"),
  "z23nosb_vs_z22nosb" = c("inf_z23", "inf_z22"),
  "z23sb_vs_z22sb" = c("inf_sb_z23", "inf_sb_z22"),
  "z23sb_vs_z23nosb" = c("inf_sb_z23", "inf_z23"),
  "z22sb_vs_z22nosb" = c("inf_sb_z22", "inf_z22"),
  "z23sb_vs_sb" = c("inf_sb_z23", "uninf_sb_none"),
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"),
  "z23sb_vs_uninf" = c("inf_sb_z23", "uninf_none"),
  "z22sb_vs_uninf" = c("inf_sb_z22", "uninf_none"),
  "sb_vs_uninf" = c("uninf_sb_none", "uninf_none"),
  "extra_z2322" = c("z23drugnodrug", "z22drugnodrug"),
  "extra_drugnodrug" = c("z23z22drug", "z23z22nodrug"))
single_tmrc2_keeper <- list(
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"))
tmrc2_drug_keepers <- list(
  "drug" = c("antimony", "none"))
tmrc2_type_keepers <- list(
  "type" = c("U937", "Macrophages"))
tmrc2_strain_keepers <- list(
  "strain" = c("z23", "z22"))
type_zymo_extra <- "zymos_vs_types = (conditionU937_z23 - conditionU937_z22) - (conditionMacrophages_z23 - conditionMacrophages_z22)"
tmrc2_typezymo_keepers <- list(
  "u937_macr" = c("Macrophages_none", "U937_none"),
  "zymo_macr" = c("Macrophages_z23", "Macrophages_z22"),
  "zymo_u937" = c("U937_z23", "U937_z22"),
  "z23_types" = c("U937_z23", "Macrophages_z23"),
  "z22_types" = c("U937_z22", "Macrophages_z22"),
  "zymos_types" = c("zymos_vs_types"))
tmrc2_typedrug_keepers <- list(
  "type_nodrug" = c("U937_none", "Macrophages_none"),
  "type_drug" = c("U937_antimony", "Macrophages_antimony"),
  "macr_drugs" = c("Macrophages_antimony", "Macrophages_none"),
  "u937_drugs" = c("U937_antimony", "U937_none"))
u937_keepers <- list(
  "z23nosb_vs_uninf" = c("inf_z23", "uninf_none"),
  "z22nosb_vs_uninf" = c("inf_z22", "uninf_none"),
  "z23nosb_vs_z22nosb" = c("inf_z23", "inf_z22"),
  "z23sb_vs_z22sb" = c("inf_sb_z23", "inf_sb_z22"),
  "z23sb_vs_z23nosb" = c("inf_sb_z23", "inf_z23"),
  "z22sb_vs_z22nosb" = c("inf_sb_z22", "inf_z22"),
  "z23sb_vs_sb" = c("inf_sb_z23", "uninf_sb_none"),
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"),
  "z23sb_vs_uninf" = c("inf_sb_z23", "uninf_none"),
  "z22sb_vs_uninf" = c("inf_sb_z22", "uninf_none"),
  "sb_vs_uninf" = c("uninf_sb_none", "uninf_none"))
## If some cases, when the set of significant genes was chosen, an
## additional filter was added to exclude genes with expression values
## less than 'high_expression' according to the
## 'high_expression_column' in the table.
high_expression <- 128
high_expression_column <- "deseq_basemean"

combined_to_tsv <- function(combined, celltype = "all") {
  keepers <- combined[["keepers"]]
  for (k in seq_len(length(keepers))) {
    kname <- names(keepers)[k]
    numerator <- keepers[[k]][1]
    denominator <- keepers[[k]][2]
    filename <- glue("analyses/macrophage_de/tsv_tables/tmrc2_{celltype}_{kname}_n{numerator}_d{denominator}-v{ver}.xlsx")
    kdata <- combined[["data"]][[kname]]
    if (is.null(kdata[["basic_num"]])) {
      next
    }
    wanted <- c("hgnc_symbol", "deseq_logfc", "deseq_adjp",
                "deseq_basemean", "deseq_num", "deseq_den")
    wanted_data <- kdata[, wanted]
    colnames(wanted_data) <- c("hgnc_symbol", "deseq_logfc", "deseq_adjp",
                               "deseq_mean", "deseq_numerator", "deseq_denominator")
    write_xlsx(data = wanted_data, excel = filename)
  }
}

write_all_gp <- function(all_gp, suffix = NULL) {
  all_written <- list()
  for (g in seq_len(length(all_gp))) {
    name <- names(all_gp)[g]
    datum <- all_gp[[name]]
    filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler-v{ver}.xlsx")
    if (!is.null(suffix)) {
      filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler{suffix}-v{ver}.xlsx")
    }
    written <- sm(write_gprofiler_data(datum, excel = filename))
    all_written[[g]] <- written
  }
  return(all_written)
}

2.4 Primary queries

There is a series of initial questions which make some sense to me, but these do not necessarily match the set of questions which are most pressing. I am hoping to pull both of these sets of queries in one.

Before extracting these groups of queries, let us invoke the all_pairwise() function and get all of the likely contrasts along with one or more extras that might prove useful (the ‘extra’ argument).

The structure of these blocks will all basically be identical:

  • Perform a set of pairwise contrasts of all the conditions against each other. Optionally use sva.
  • Given that result, dump it in its entirety to an xlsx file in the analyses/ directory.
  • Given those combined tables, extract from them the set deemed ‘significant’ by whatever criteria we want to try. (Usually |lfc| >= 1.0, adjusted p <= 0.05; but potentially also expression >= x and sometimes a set of less stringent values (|lfc| >= 0.6))
  • Given one or more gene sets deemed ‘significant’ pass them to gProfiler2 and see what pops out.

2.4.1 Combined U937 and Macrophages: Compare drug effects

When we have the u937 cells in the same dataset as the macrophages, that provides an interesting opportunity to see if we can observe drug-dependant effects which are shared across both cell types.

Note to self: given the changes to hpgltools I may need to specify the statistical model string when I am using svaseq for some/many/all of these comparisons.

drug_de <- all_pairwise(all_human, filter = TRUE, model_svs = "svaseq", do_noiseq = FALSE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_human' not found
drug_de
## Error: object 'drug_de' not found
drug_table <- combine_de_tables(
  drug_de, keepers = tmrc2_drug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_drug_comparison-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'drug_de' not found
drug_table
## Error: object 'drug_table' not found
#combined_to_tsv(drug_table, celltype = "all")

drug_sig <- extract_significant_genes(
  drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_sig-v{ver}.xlsx"))
## Error: object 'drug_table' not found
drug_sig
## Error: object 'drug_sig' not found
drug_highsig <- extract_significant_genes(
  drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_highsig-v{ver}.xlsx"))
## Error: object 'drug_table' not found
drug_highsig
## Error: object 'drug_highsig' not found
drug_lesssig <- extract_significant_genes(
  drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_lesssig-v{ver}.xlsx"))
## Error: object 'drug_table' not found
drug_lesssig
## Error: object 'drug_lesssig' not found

2.4.1.1 gProfiler2 results of the significant drug genes

all_drug_gp <- all_gprofiler(drug_sig, enrich_id_column = "hgnc_symbol")
## Error: object 'drug_sig' not found
all_drug_gp
## Error: object 'all_drug_gp' not found
written <- write_all_gp(all_drug_gp)
## Error: object 'all_drug_gp' not found
all_drug_lesssig <- all_gprofiler(drug_lesssig, enrich_id_column = "hgnc_symbol")
## Error: object 'drug_lesssig' not found
written <- write_all_gp(all_drug_lesssig, suffix = "_lfc0.6_")
## Error: object 'all_drug_lesssig' not found

2.4.2 Combined U937 and Macrophages: compare cell types

There are a couple of ways one might want to directly compare the two cell types.

  • Given that the variance between the two celltypes is so huge, just compare all samples.
  • One might want to compare them with the interaction effects of drug/zymodeme.
type_de <- all_pairwise(all_human_types, filter = TRUE,
                        model_svs = "svaseq", do_noiseq = FALSE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'all_human_types' not found
type_de
## Error: object 'type_de' not found
type_table <- combine_de_tables(
  type_de, keepers = tmrc2_type_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_comparison-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'type_de' not found
type_table
## Error: object 'type_table' not found
#combined_to_tsv(type_table, celltype = "all")

type_sig <- extract_significant_genes(
  type_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_sig-v{ver}.xlsx"))
## Error: object 'type_table' not found
type_sig
## Error: object 'type_sig' not found
type_highsig <- extract_significant_genes(
  type_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_highsig-v{ver}.xlsx"))
## Error: object 'type_table' not found
type_highsig
## Error: object 'type_highsig' not found
type_lesssig <- extract_significant_genes(
  type_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_lesssig-v{ver}.xlsx"))
## Error: object 'type_table' not found
type_sig
## Error: object 'type_sig' not found

2.4.2.1 Combined factors of interest: celltype+zymodeme

Given the above explicit comparison of all samples comprising the two cell types, now let us look at the drug treatment+zymodeme status with all samples, macrophages and U937.

type_zymo_de <- all_pairwise(type_zymo, filter = TRUE, model_svs = "svaseq",
                             do_noiseq = FALSE,
                             extra_contrasts = type_zymo_extra)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'type_zymo' not found
type_zymo_de
## Error: object 'type_zymo_de' not found
type_zymo_table <- combine_de_tables(
  type_zymo_de, keepers = tmrc2_typezymo_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_zymo_comparison-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'type_zymo_de' not found
#combined_to_tsv(type_zymo_table, celltype = "all")

type_zymo_sig <- extract_significant_genes(
  type_zymo_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_sig-v{ver}.xlsx"))
## Error: object 'type_zymo_table' not found
type_zymo_sig
## Error: object 'type_zymo_sig' not found
type_zymo_highsig <- extract_significant_genes(
  type_zymo_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_highsig-v{ver}.xlsx"))
## Error: object 'type_zymo_table' not found
type_zymo_lesssig <- extract_significant_genes(
  type_zymo_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_lesssig-v{ver}.xlsx"))
## Error: object 'type_zymo_table' not found
type_zymo_lesssig
## Error: object 'type_zymo_lesssig' not found

2.4.2.2 Combined factors of interest: celltype+drug

The ‘type_drug’ datastructure is the same as above, but the condition is created from the concatenation of the cell type and drug treatment.

type_drug_de <- all_pairwise(type_drug, filter = TRUE, model_svs = "svaseq",
                             model_fstring = "~ 0 + condition")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'type_drug' not found
type_drug_de
## Error: object 'type_drug_de' not found
type_drug_table <- combine_de_tables(
  type_drug_de, keepers = tmrc2_typedrug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_drug_comparison-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'type_drug_de' not found
type_drug_table
## Error: object 'type_drug_table' not found
#combined_to_tsv(type_drug_table, celltype = "all")

type_drug_sig <- extract_significant_genes(
  type_drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_sig-v{ver}.xlsx"))
## Error: object 'type_drug_table' not found
type_drug_sig
## Error: object 'type_drug_sig' not found
type_drug_highsig <- extract_significant_genes(
  type_drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_highsig-v{ver}.xlsx"))
## Error: object 'type_drug_table' not found
type_drug_highsig
## Error: object 'type_drug_highsig' not found
type_drug_lesssig <- extract_significant_genes(
  type_drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_lesssig-v{ver}.xlsx"))
## Error: object 'type_drug_table' not found
type_drug_lesssig
## Error: object 'type_drug_lesssig' not found

3 Individual cell types

At this point, I think it is fair to say that the two cell types are sufficiently different that they do not really belong together in a single analysis.

3.1 drug or strain effects, single cell type

One of the queries Najib asked which I think I misinterpreted was to look at drug and/or strain effects. My interpretation is somewhere below and was not what he was looking for. Instead, he was looking to see all(macrophage) drug/nodrug and all(macrophage) z23/z22 and compare them to each other. It may be that this is still a wrong interpretation, if so the most likely comparison is either:

  • (z23drug/z22drug) / (z23nodrug/z22nodrug), or perhaps
  • (z23drug/z23nodrug) / (z22drug/z22nodrug),

I am not sure those confuse me, and at least one of them is below

3.2 Macrophages

In these blocks we will explicitly query only one factor at a time, drug and strain. The eventual goal is to look for effects of drug treatment and/or strain treatment which are shared?

3.2.1 Macrophage Drug only

Thus we will start with the pure drug query. In this block we will look only at the drug/nodrug effect.

hs_macr_drug_de <- all_pairwise(hs_macr_drug_expt, filter = TRUE, model_svs = "svaseq",
                                model_fstring = "~ 0 + condition")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr_drug_expt' not found
hs_macr_drug_de
## Error: object 'hs_macr_drug_de' not found
hs_macr_drug_table <- combine_de_tables(
  hs_macr_drug_de, keepers = tmrc2_drug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_onlydrug_table-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_macr_drug_de' not found
hs_macr_drug_table
## Error: object 'hs_macr_drug_table' not found
#combined_to_tsv(hs_macr_drug_table, celltype = "macrophage")

hs_macr_drug_sig <- extract_significant_genes(
  hs_macr_drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_sig-v{ver}.xlsx"))
## Error: object 'hs_macr_drug_table' not found
hs_macr_drug_sig
## Error: object 'hs_macr_drug_sig' not found
hs_macr_drug_highsig <- extract_significant_genes(
  hs_macr_drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_highsig-v{ver}.xlsx"))
## Error: object 'hs_macr_drug_table' not found
hs_macr_drug_highsig
## Error: object 'hs_macr_drug_highsig' not found
## Creating the following to see how it affects gProfiler.
hs_macr_drug_lesssig <- extract_significant_genes(
  hs_macr_drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_sig_lfc0.6-v{ver}.xlsx"))
## Error: object 'hs_macr_drug_table' not found

3.2.2 Macrophage Strain only

In a similar fashion, let us look for effects which are observed when we consider only the strain used during infection.

hs_macr_strain_de <- all_pairwise(hs_macr_strain_expt, filter = TRUE, model_svs = "svaseq",
                                  model_fstring = "~ 0 + condition")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr_strain_expt' not found
hs_macr_strain_de
## Error: object 'hs_macr_strain_de' not found
hs_macr_strain_table <- combine_de_tables(
  hs_macr_strain_de, keepers = tmrc2_strain_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_onlystrain_table-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_macr_strain_de' not found
hs_macr_strain_table
## Error: object 'hs_macr_strain_table' not found
combined_to_tsv(hs_macr_strain_table, celltype = "macrophage")
## Error: object 'hs_macr_strain_table' not found
hs_macr_strain_sig <- extract_significant_genes(
  hs_macr_strain_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_sig-v{ver}.xlsx"))
## Error: object 'hs_macr_strain_table' not found
hs_macr_strain_sig
## Error: object 'hs_macr_strain_sig' not found
hs_macr_strain_highsig <- extract_significant_genes(
  hs_macr_strain_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_highsig-v{ver}.xlsx"))
## Error: object 'hs_macr_strain_table' not found
hs_macr_strain_highsig
## Error: object 'hs_macr_strain_highsig' not found
hs_macr_strain_lesssig <- extract_significant_genes(
  hs_macr_strain_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_lesssig-v{ver}.xlsx"))
## Error: object 'hs_macr_strain_table' not found
hs_macr_strain_lesssig
## Error: object 'hs_macr_strain_lesssig' not found

3.2.3 Compare Drug and Strain Effects

Now let us consider the above two comparisons together. First, I will plot the logFC values of them against each other (drug on x-axis and strain on the y-axis). Then we can extract the significant genes in a few combined categories of interest. I assume these will focus exclusively on the categories which include the introduction of the drug.

drug_strain_comp_df <- merge(hs_macr_drug_table[["data"]][["drug"]],
                             hs_macr_strain_table[["data"]][["strain"]],
                             by = "row.names")
## Error: object 'hs_macr_drug_table' not found
drug_strain_comp_plot <- plot_linear_scatter(
  drug_strain_comp_df[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'drug_strain_comp_df' not found
## Contrasts: antimony/none, z23/z22; x-axis: drug, y-axis: strain
## top left: higher no drug, z23; top right: higher drug z23
## bottom left: higher no drug, z22; bottom right: higher drug z22
drug_strain_comp_plot[["scatter"]]
## Error: object 'drug_strain_comp_plot' not found

As I noted in the comments above, some quadrants of the scatter plot are likely to be of greater interest to us than others (the right side). Because I get confused sometimes, the following block will explicitly name the categories of likely interest, then ask which genes are shared among them, and finally use UpSetR to extract the various gene intersection/union categories.

higher_drug <- hs_macr_drug_sig[["deseq"]][["downs"]][[1]]
## Error: object 'hs_macr_drug_sig' not found
higher_nodrug <- hs_macr_drug_sig[["deseq"]][["ups"]][[1]]
## Error: object 'hs_macr_drug_sig' not found
higher_z23 <- hs_macr_strain_sig[["deseq"]][["ups"]][[1]]
## Error: object 'hs_macr_strain_sig' not found
higher_z22 <- hs_macr_strain_sig[["deseq"]][["downs"]][[1]]
## Error: object 'hs_macr_strain_sig' not found
sum(rownames(higher_drug) %in% rownames(higher_z23))
## Error: object 'higher_drug' not found
sum(rownames(higher_drug) %in% rownames(higher_z22))
## Error: object 'higher_drug' not found
sum(rownames(higher_nodrug) %in% rownames(higher_z23))
## Error: object 'higher_nodrug' not found
sum(rownames(higher_nodrug) %in% rownames(higher_z22))
## Error: object 'higher_nodrug' not found
drug_z23_lst <- list("drug" = rownames(higher_drug),
                     "z23" = rownames(higher_z23))
## Error: object 'higher_drug' not found
upset_input <- UpSetR::fromList(drug_z23_lst)
## Error: object 'drug_z23_lst' not found
higher_drug_z23 <- upset(upset_input, text.scale = 2)
## Error: object 'upset_input' not found
higher_drug_z23
## Error: object 'higher_drug_z23' not found
drug_z23_shared_genes <- overlap_groups(drug_z23_lst)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'drug_z23_lst' not found
shared_genes_drug_z23 <- overlap_geneids(drug_z23_shared_genes, "drug:z23")
## Error: object 'drug_z23_shared_genes' not found
shared_genes_drug_z23 <- attr(drug_z23_shared_genes, "elements")[drug_z23_shared_genes[["drug:z23"]]]
## Error: object 'drug_z23_shared_genes' not found
drug_z22_lst <- list("drug" = rownames(higher_drug),
                     "z22" = rownames(higher_z22))
## Error: object 'higher_drug' not found
higher_drug_z22 <- upset(UpSetR::fromList(drug_z22_lst), text.scale = 2)
## Error: object 'drug_z22_lst' not found
higher_drug_z22
## Error: object 'higher_drug_z22' not found
drug_z22_shared_genes <- overlap_groups(drug_z22_lst)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'drug_z22_lst' not found
shared_genes_drug_z22 <- overlap_geneids(drug_z22_shared_genes, "drug:z22")
## Error: object 'drug_z22_shared_genes' not found
shared_genes_drug_z22 <- attr(drug_z22_shared_genes, "elements")[drug_z22_shared_genes[["drug:z22"]]]
## Error: object 'drug_z22_shared_genes' not found

3.2.4 Perform gProfiler on drug/strain effect shared genes

Now that we have some populations of genes which are shared across the drug/strain effects, let us pass them to some GSEA analyses and see what pops out.

wanted <- drug_z23_shared_genes[["drug:z23"]]
## Error: object 'drug_z23_shared_genes' not found
shared_genes_drug_z23 <- attr(drug_z23_shared_genes, "elements")[wanted]
## Error: object 'drug_z23_shared_genes' not found
shared_drug_z23_gp <- simple_gprofiler(shared_genes_drug_z23)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'shared_genes_drug_z23' not found
shared_drug_z23_gp[["pvalue_plots"]][["MF"]]
## Error: object 'shared_drug_z23_gp' not found
shared_drug_z23_gp[["pvalue_plots"]][["BP"]]
## Error: object 'shared_drug_z23_gp' not found
shared_drug_z23_gp[["pvalue_plots"]][["REAC"]]
## Error: object 'shared_drug_z23_gp' not found
wanted <- drug_z22_shared_genes[["drug:z22"]]
## Error: object 'drug_z22_shared_genes' not found
shared_genes_drug_z22 <- attr(drug_z22_shared_genes, "elements")[wanted]
## Error: object 'drug_z22_shared_genes' not found
shared_drug_z22_gp <- simple_gprofiler(shared_genes_drug_z22)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'shared_genes_drug_z22' not found
shared_drug_z22_gp[["pvalue_plots"]][["BP"]]
## Error: object 'shared_drug_z22_gp' not found

4 Our main question of interest

The data structure hs_macr contains our primary macrophages, which are, as shown above, the data we can really sink our teeth into.

Note, we expect some errors when running the combine_de_tables() because not all methods I use are comfortable using the ratio or ratios contrasts we added in the ‘extras’ argument. As a result, when we combine them into the larger output tables, those peculiar contrasts fail. This does not stop it from writing the rest of the results, however.

#test = deseq_pairwise(normalize_expt(hs_macr, filter=TRUE),
#                      model_svs = "svaseq", filter = TRUE,
#                      extra_contrasts = tmrc2_human_extra)

hs_macr_de_noextra <- all_pairwise(hs_macr, model_svs = "svaseq", model_fstring = "~ 0 + condition", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
hs_macr_de <- all_pairwise(hs_macr, model_svs = "svaseq", model_fstring = "~ 0 + condition",
                           filter = TRUE, extra_contrasts = tmrc2_human_extra)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
hs_macr_de
## Error: object 'hs_macr_de' not found
hs_single_table <- combine_de_tables(
  hs_macr_de, keepers = single_tmrc2_keeper,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_z22sb_sb-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_macr_de' not found
hs_single_table
## Error: object 'hs_single_table' not found
hs_macr_table <- combine_de_tables(
  hs_macr_de, keepers = tmrc2_human_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_table_macr_only-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_macr_de' not found
hs_macr_table
## Error: object 'hs_macr_table' not found
#combined_to_tsv(hs_macr_table, "macrophage")

hs_macr_sig <- extract_significant_genes(
  hs_macr_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_sig-v{ver}.xlsx"))
## Error: object 'hs_macr_table' not found
hs_macr_sig
## Error: object 'hs_macr_sig' not found
hs_macr_highsig <- extract_significant_genes(
  hs_macr_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_highsig-v{ver}.xlsx"))
## Error: object 'hs_macr_table' not found
hs_macr_highsig
## Error: object 'hs_macr_highsig' not found
hs_macr_lesssig <- extract_significant_genes(
  hs_macr_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_sig_lfc0.6-v{ver}.xlsx"))
## Error: object 'hs_macr_table' not found
hs_macr_lesssig
## Error: object 'hs_macr_lesssig' not found

4.1 gene group upset

4.1.1 2.3 vs 2.2 up and down vs. uninfected

This is my version of the Venn diagram which includes the text:

“Differentially expressed genes in macrophages infected with subpopulations 2.2 or 2.3. Volcano plots contrast of: A. Venn diagram for upregulated and downregulated genes by infection with 2.3 and 2.2 strains. B. infected cells with 2.3 strains and uninfected cells; C. infected cells with 2.2 strains and uninfected cells; D. infected cells with 2.3 strains and infected cells with 2.2 strains”

The following upset plot is currently Figure 2E.

nodrug_upset <- upsetr_combined_de(hs_macr_table,
                                   desired_contrasts = c("z22nosb_vs_uninf", "z23nosb_vs_uninf"))
## Error: object 'hs_macr_table' not found
pp(file = "images/nodrug_upset.svg")
nodrug_upset[["plot"]]
## Error: object 'nodrug_upset' not found
dev.off()
## png 
##   2
nodrug_upset
## Error: object 'nodrug_upset' not found

4.1.1.1 A point of interest while Olga visits Umd

Najib and Olga asked about pulling the 9 gene IDs which are in the peculiar situation of increased expression in z2.2/uninf and decreased in z2.3/uninf. In the previous upset plot, these are visible in the 6th bar. I can access these via the attr() function, which I should admit I can never remember how to use, so I am going to use the code under the ‘Compare(no)Sb z2.3/z2.2 treatment’ heading to remember how to extract these genes.

all_groups <- nodrug_upset[["groups"]]
## Error: object 'nodrug_upset' not found
wanted_group <- "z23nosb_vs_uninf_down:z22nosb_vs_uninf_up"
gene_idx <- all_groups[[wanted_group]]
## Error: object 'all_groups' not found
wanted_genes <- attr(all_groups, "elements")[gene_idx]
## Error: object 'all_groups' not found
wanted_genes
## Error: object 'wanted_genes' not found
gene_symbol_idx <- rownames(fData(hs_macr)) %in% as.character(wanted_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'hs_macr' not found
fData(hs_macr)[gene_symbol_idx, "hgnc_symbol"]
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'hs_macr' not found
  • ABCB5: ATB Binding Cassette Subfamily B Member #5, wide range of functions in this diverse paralogous family. Associated with skin diseases (melanoma and Epidermolysis Bullosa; participate in ATP-dependent transmembrane transport).
  • RFX4: Regulatory Factor X #4: transcription factor.
  • CA14: Carbonic anhydrase #14: Zync metalloenzyme catalyzes reversible hydration of CO2. This gene looks pretty neat, but not really relevant to anything we are likely to care about.
  • EGR1: Early Growth Response Protein #1: Another Tx factor (zinc-finger) – important for cell survival/proliferation/cell death. Presumably important for healing?
  • MCF2L: MCF.2 Cell Line Derived Transforming Sequence Like? guanine nucleotide exchange factor interacting with GTP-bound Rac1. Apparently associated with ostroarthritis; potentially relevant to regulation of RHOA and CDC42 signalling.
  • DNASE1L3: Deoxyribonuclease I family member: not inhibited by actin, breaks down DNA during apoptosis. Important during necrosis.
  • FOS: Proto-Oncogene, AP-1 Transcription Factor: leucine zipper dimerizes with JUN family proteins, forming tx factor complex AP-1. Important for cell proliferation, differentiation, and transformation.
  • IFITM10: Interferon-Induced Transmembrane Protein #10
  • PKD1L3: Polycystin 1 Like #3, Transient Receptor Potential Channel Interacting: 11 transmembrane domain protein which might help create cation channels.

As some comparison points, the Venn in the current figure has:

  • 387 up z2.3
  • 259 up z2.2
  • 83 shared up z2.3 and z2.2
  • 247 down z2.3
  • 3 down z2.2
  • 3 shared down z2.3 and z2.2

4.1.2 2.2 and 2.3 with SbV vs 2.2 and 2.3 without SbV

This is my version of the Venn with the text:

“Differentially expressed genes in macrophages infected with subpopulations 2.2 or 2.3, in presence of SbV. Volcano plots contrast of: A. infected cells with 2.3 strains + SbV and infected cells with 2.3 strains; B. infected cells with 2.2 strains + SbV and infected cells with 2.2 strains; C. infected cells with 2.3 strains + SbV and infected cells with 2.2 strains + SbV. D. Venn diagram for upregulated and downregulated genes by infection with 2.3+SbV and 2.2+SbV strains.”

A query from Olga (20240801): Please include in the upset in figure 3 the contrast of uninfected cells + SbV vs uninfected without SbV.

## I keep mis-interpreting this text, it is z2.3/z2.3SbV and z2.2/z2.2SbV
drugnodrug_upset <- upsetr_combined_de(hs_macr_table,
                                       desired_contrasts = c("z23sb_vs_z23nosb", "z22sb_vs_z22nosb"))
## Error: object 'hs_macr_table' not found
pp(file = "images/drugnodrug_upset.pdf")
drugnodrug_upset[["plot"]]
## Error: object 'drugnodrug_upset' not found
dev.off()
## png 
##   2
drugnodrug_upset
## Error: object 'drugnodrug_upset' not found
drugnodrug_uninf_contrasts <- c("z23sb_vs_z23nosb", "z22sb_vs_z22nosb", "sb_vs_uninf")
drugnodrug_upset_with_uninf <- upsetr_combined_de(hs_macr_table,
                                       desired_contrasts = drugnodrug_uninf_contrasts)
## Error: object 'hs_macr_table' not found
pp(file = "figures/drugnodrug_with_uninf_upset.svg")
drugnodrug_upset_with_uninf[["plot"]]
## Error: object 'drugnodrug_upset_with_uninf' not found
dev.off()
## png 
##   2
drugnodrug_upset_with_uninf
## Error: object 'drugnodrug_upset_with_uninf' not found

For some comparison points, the venn image has:

  • 222 up z2.3 SbV
  • 134 up z2.2 SbV
  • 182 down z2.3 SbV
  • 396 down z2.2 SbV
  • 605 shared down z2.2 and z2.3 SbV
  • 34 shared down z2.2 SbV and up z2.3 SbV
  • 363 shared up z2.2 SbV and z2.3 SbV

4.1.3 Compare z2.2SbV vs SbV and z2.3SbV and SbV

drug_upset <- upsetr_combined_de(hs_macr_table,
                                 desired_contrasts = c("z22sb_vs_sb", "z23sb_vs_sb"))
## Error: object 'hs_macr_table' not found
pp(file = "images/drug_upset.pdf")
drug_upset[["plot"]]
## Error: object 'drug_upset' not found
dev.off()
## png 
##   2
drug_upset
## Error: object 'drug_upset' not found

4.2 Significance barplot of interest

Olga kindly sent a set of particularly interesting contrasts and colors for a significance barplot, they include the following:

  • z2.3 vs. uninfected.
  • z2.2 vs. uninfected.
  • z2.3 vs z2.2
  • z2.3Sbv vs z2.3
  • z2.2Sbv vs z2.2
  • z2.3Sbv vs z2.2Sbv
  • Sbv vs uninfected.

The existing set of ‘keepers’ exvised to these is taken from the extant set of ‘tmrc2_human_keepers’ and is as follows:

barplot_keepers <- list(
  ## z2.3 vs uninfected
  "z23nosb_vs_uninf" = c("infz23", "uninfnone"),
  ## z2.2 vs uninfected
  "z22nosb_vs_uninf" = c("infz22", "uninfnone"),
  ## z2.3 vs z2.2
  "z23nosb_vs_z22nosb" = c("infz23", "infz22"),
  ## z2.3Sbv vs z2.3
  "z23sb_vs_z23nosb" = c("infsbz23", "infz23"),
  ## z2.2Sbv vs z2.2
  "z22sb_vs_z22nosb" = c("infsbz22", "infz22"),
  ## z2.3Sbv vs z2.2Sbv
  "z23sb_vs_z22sb" = c("infsbz23", "infsbz22"),
  ## Sbv vs uninfected.
  "sb_vs_uninf" = c("uninfsbnone", "uninfnone"))
barplot_combined <- combine_de_tables(
  hs_macr_de, keepers = barplot_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_7contrasts-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_macr_de' not found

Now let us use the colors suggested by Olga to make a barplot of these…

color_list <-  c( "#de8bf9", "#ad07e3","#410257", "#ffa0a0", "#f94040", "#a00000")
barplot_sig <- extract_significant_genes(
  barplot_combined, color_list = color_list, according_to = "deseq",
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_7contrasts_sig-v{ver}.xlsx"))
## Error: object 'barplot_combined' not found
barplot_sig
## Error: object 'barplot_sig' not found

5 PROPER

In our last meeting there were some questions about the statistical power of different future experimental designs. One thing I can do is to use PROPER to estimate the power of an extant dataset and infer from that the likely power of other designs.

In order to use proper, one must feed it one or more DE tables.

power_estimate <- simple_proper(hs_single_table)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'hs_single_table' not found
power_estimate[[1]][["power_plot"]]
## Error: object 'power_estimate' not found
power_estimate[[1]][["powertd_plot"]]
## Error: object 'power_estimate' not found
power_estimate[[1]][["powerfd_plot"]]
## Error: object 'power_estimate' not found

6 Our main questions in U937

Let us do the same comparisons in the U937 samples, though I will not do the extra contrasts, primarily because I think the dataset is less likely to support them.

u937_de <- all_pairwise(u937_expt, model_svs = "svaseq",
                        filter = TRUE, do_noiseq = FALSE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'u937_expt' not found
u937_de
## Error: object 'u937_de' not found
u937_table <- combine_de_tables(
  u937_de, keepers = u937_keepers,
  excel = glue("analyses/macrophage_de/de_tables/u937_drug_zymo_table-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'u937_de' not found
u937_table
## Error: object 'u937_table' not found
combined_to_tsv(u937_table, celltype = "u937")
## Error: object 'u937_table' not found
u937_sig <- extract_significant_genes(
  u937_table,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_sig-v{ver}.xlsx"))
## Error: object 'u937_table' not found
u937_sig
## Error: object 'u937_sig' not found
u937_highsig <- extract_significant_genes(
  u937_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_highsig-v{ver}.xlsx"))
## Error: object 'u937_table' not found
u937_highsig
## Error: object 'u937_highsig' not found
u937_lesssig <- extract_significant_genes(
  u937_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_lesssig-v{ver}.xlsx"))
## Error: object 'u937_table' not found
u937_lesssig
## Error: object 'u937_lesssig' not found

7 Compare (no)Sb z2.3/z2.2 treatments among macrophages

In the following block, I will jump back to the macrophage samples and look for genes which are shared/unique when comparing z2.3/z2.2 for the drug treated samples and the untreated samples.

upset_plots_hs_macr <- upsetr_sig(
  hs_macr_sig, both = TRUE,
  contrasts = c("z23sb_vs_z22sb", "z23nosb_vs_z22nosb"))
## Error: object 'hs_macr_sig' not found
upset_plots_hs_macr[["both"]]
## Error: object 'upset_plots_hs_macr' not found
groups <- upset_plots_hs_macr[["both_groups"]]
## Error: object 'upset_plots_hs_macr' not found
shared_genes <- attr(groups, "elements")[groups[[2]]] %>%
  gsub(pattern = "^gene:", replacement = "")
## Error: object 'groups' not found
length(shared_genes)
## Error: object 'shared_genes' not found
shared_gp <- simple_gprofiler(shared_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'shared_genes' not found
shared_gp[["pvalue_plots"]][["MF"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["BP"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["REAC"]]
## Error: object 'shared_gp' not found
drug_genes <- attr(groups, "elements")[groups[["z23sb_vs_z22sb"]]] %>%
  gsub(pattern = "^gene:", replacement = "")
## Error: object 'groups' not found
drugonly_gp <- simple_gprofiler(drug_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'drug_genes' not found
drugonly_gp[["pvalue_plots"]][["BP"]]
## Error: object 'drugonly_gp' not found

I want to try something, directly include the u937 data in this. Thus, in the following block I will repeat but compare all samples and the U937 using the same logic.

both_sig <- hs_macr_sig
## Error: object 'hs_macr_sig' not found
names(both_sig[["deseq"]][["ups"]]) <- paste0("macr_", names(both_sig[["deseq"]][["ups"]]))
## Error: object 'both_sig' not found
names(both_sig[["deseq"]][["downs"]]) <- paste0("macr_", names(both_sig[["deseq"]][["downs"]]))
## Error: object 'both_sig' not found
u937_deseq <- u937_sig[["deseq"]]
## Error: object 'u937_sig' not found
names(u937_deseq[["ups"]]) <- paste0("u937_", names(u937_deseq[["ups"]]))
## Error: object 'u937_deseq' not found
names(u937_deseq[["downs"]]) <- paste0("u937_", names(u937_deseq[["downs"]]))
## Error: object 'u937_deseq' not found
both_sig[["deseq"]][["ups"]] <- c(both_sig[["deseq"]][["ups"]], u937_deseq[["ups"]])
## Error: object 'both_sig' not found
both_sig[["deseq"]][["downs"]] <- c(both_sig[["deseq"]][["ups"]], u937_deseq[["downs"]])
## Error: object 'both_sig' not found
summary(both_sig[["deseq"]][["ups"]])
## Error: object 'both_sig' not found
upset_plots_both <- upsetr_sig(
  both_sig, both = TRUE,
  contrasts = c("macr_z23sb_vs_z22sb", "macr_z23nosb_vs_z22nosb",
                "u937_z23sb_vs_z22sb", "u937_z23nosb_vs_z22nosb"))
## Error: object 'both_sig' not found
upset_plots_both[["both"]]
## Error: object 'upset_plots_both' not found

7.1 Compare DE results from macrophages and U937 samples

Looking a bit more closely at these, I think the u937 data is too sparse to effectively compare.

macr_u937_comparison <- compare_de_results(hs_macr_table, u937_table)
## Error: object 'hs_macr_table' not found
macr_u937_comparison[["lfc_heat"]]
## Error: object 'macr_u937_comparison' not found
macr_u937_venns <- compare_significant_contrasts(hs_macr_sig, second_sig_tables = u937_sig,
                                                 contrasts = "z23sb_vs_z23nosb")
## Error: object 'hs_macr_sig' not found
macr_u937_venns[["up_plot"]]
## Error: object 'macr_u937_venns' not found
macr_u937_venns[["down_plot"]]
## Error: object 'macr_u937_venns' not found
macr_u937_venns_v2 <- compare_significant_contrasts(
  hs_macr_sig, second_sig_tables = u937_sig, contrasts = "z22sb_vs_z22nosb")
## Error: object 'hs_macr_sig' not found
macr_u937_venns_v2[["up_plot"]]
## Error: object 'macr_u937_venns_v2' not found
macr_u937_venns_v2[["down_plot"]]
## Error: object 'macr_u937_venns_v2' not found
macr_u937_venns_v3 <- compare_significant_contrasts(
  hs_macr_sig, second_sig_tables = u937_sig, contrasts = "sb_vs_uninf")
## Error: object 'hs_macr_sig' not found
macr_u937_venns_v3[["up_plot"]]
## Error: object 'macr_u937_venns_v3' not found
macr_u937_venns_v3[["down_plot"]]
## Error: object 'macr_u937_venns_v3' not found

7.2 Compare macrophage/u937 with respect to z2.3/z2.2

comparison_df <- merge(hs_macr_table[["data"]][["z23sb_vs_z22sb"]],
                       u937_table[["data"]][["z23sb_vs_z22sb"]],
                       by = "row.names")
## Error: object 'hs_macr_table' not found
macru937_z23z22_plot <- plot_linear_scatter(comparison_df[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'comparison_df' not found
macru937_z23z22_plot[["scatter"]]
## Error: object 'macru937_z23z22_plot' not found
comparison_df <- merge(hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]],
                       u937_table[["data"]][["z23nosb_vs_z22nosb"]],
                       by = "row.names")
## Error: object 'hs_macr_table' not found
macru937_z23z22_plot <- plot_linear_scatter(comparison_df[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'comparison_df' not found
macru937_z23z22_plot[["scatter"]]
## Error: object 'macru937_z23z22_plot' not found

8 Add donor to the contrasts, no sva

In the following block, I will change the sample condition to include the donor.

no_power_fact <- paste0(pData(hs_macr)[["donor"]], "_",
                        pData(hs_macr)[["condition"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
table(pData(hs_macr)[["donor"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
table(no_power_fact)
## Error: object 'no_power_fact' not found
hs_nopower <- set_expt_conditions(hs_macr, fact = no_power_fact)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
hs_nopower <- subset_expt(hs_nopower, subset = "macrophagezymodeme!='none'")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'hs_nopower' not found
hs_nopower_nosva_de <- all_pairwise(hs_nopower, model_svs = FALSE, filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_nopower' not found
nopower_keepers <- list(
  "d01_zymo" = c("d01infz23", "d01infz22"),
  "d01_sbzymo" = c("d01infsbz23", "d01infsbz22"),
  "d02_zymo" = c("d02infz23", "d02infz22"),
  "d02_sbzymo" = c("d02infsbz23", "d02infsbz22"),
  "d09_zymo" = c("d09infz23", "d09infz22"),
  "d09_sbzymo" = c("d09infsbz23", "d09infsbz22"),
  "d81_zymo" = c("d81infz23", "d81infz22"),
  "d81_sbzymo" = c("d81infsbz23", "d81infsbz22"))
hs_nopower_nosva_table <- combine_de_tables(
  hs_nopower_nosva_de, keepers = nopower_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_nopower_table-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_nopower_nosva_de' not found
## extra_contrasts = extra)
hs_nopower_nosva_sig <- extract_significant_genes(
  hs_nopower_nosva_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_nopower_nosva_sig-v{ver}.xlsx"))
## Error: object 'hs_nopower_nosva_table' not found
d01d02_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d01_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d02_zymo"]],
                                by = "row.names")
## Error: object 'hs_nopower_nosva_table' not found
d0102_zymo_nosva_plot <- plot_linear_scatter(d01d02_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd01d02_zymo_nosva_comp' not found
d0102_zymo_nosva_plot[["scatter"]]
## Error: object 'd0102_zymo_nosva_plot' not found
d0102_zymo_nosva_plot[["correlation"]]
## Error: object 'd0102_zymo_nosva_plot' not found
d0102_zymo_nosva_plot[["lm_rsq"]]
## Error: object 'd0102_zymo_nosva_plot' not found
d09d81_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d09_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d81_zymo"]],
                                by = "row.names")
## Error: object 'hs_nopower_nosva_table' not found
d0981_zymo_nosva_plot <- plot_linear_scatter(d09d81_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd09d81_zymo_nosva_comp' not found
d0981_zymo_nosva_plot[["scatter"]]
## Error: object 'd0981_zymo_nosva_plot' not found
d0981_zymo_nosva_plot[["correlation"]]
## Error: object 'd0981_zymo_nosva_plot' not found
d0981_zymo_nosva_plot[["lm_rsq"]]
## Error: object 'd0981_zymo_nosva_plot' not found
d01d81_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d01_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d81_zymo"]],
                                by = "row.names")
## Error: object 'hs_nopower_nosva_table' not found
d0181_zymo_nosva_plot <- plot_linear_scatter(d01d81_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd01d81_zymo_nosva_comp' not found
d0181_zymo_nosva_plot[["scatter"]]
## Error: object 'd0181_zymo_nosva_plot' not found
d0181_zymo_nosva_plot[["correlation"]]
## Error: object 'd0181_zymo_nosva_plot' not found
d0181_zymo_nosva_plot[["lm_rsq"]]
## Error: object 'd0181_zymo_nosva_plot' not found
upset_plots_nosva <- upsetr_sig(hs_nopower_nosva_sig, both = TRUE,
                                contrasts = c("d01_zymo", "d02_zymo", "d09_zymo", "d81_zymo"))
## Error: object 'hs_nopower_nosva_sig' not found
upset_plots_nosva[["up"]]
## Error: object 'upset_plots_nosva' not found
upset_plots_nosva[["down"]]
## Error: object 'upset_plots_nosva' not found
upset_plots_nosva[["both"]]
## Error: object 'upset_plots_nosva' not found
## The 7th element in the both groups list is the set shared among all donors.
## I don't feel like writing out x:y:z:a
groups <- upset_plots_nosva[["both_groups"]]
## Error: object 'upset_plots_nosva' not found
shared_genes <- attr(groups, "elements")[groups[[7]]] %>%
  gsub(pattern = "^gene:", replacement = "")
## Error: object 'groups' not found
shared_gp <- simple_gprofiler(shared_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'shared_genes' not found
shared_gp[["pvalue_plots"]][["MF"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["BP"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["REAC"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["WP"]]
## Error: object 'shared_gp' not found

9 Add donor to the contrasts, sva

Same deal as the last block, but this time add SVA into the mix!

hs_nopower_sva_de <- all_pairwise(hs_nopower, model_svs = "svaseq", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_nopower' not found
nopower_keepers <- list(
  "d01_zymo" = c("d01infz23", "d01infz22"),
  "d01_sbzymo" = c("d01infsbz23", "d01infsbz22"),
  "d02_zymo" = c("d02infz23", "d02infz22"),
  "d02_sbzymo" = c("d02infsbz23", "d02infsbz22"),
  "d09_zymo" = c("d09infz23", "d09infz22"),
  "d09_sbzymo" = c("d09infsbz23", "d09infsbz22"),
  "d81_zymo" = c("d81infz23", "d81infz22"),
  "d81_sbzymo" = c("d81infsbz23", "d81infsbz22"))
hs_nopower_sva_table <- combine_de_tables(
  hs_nopower_sva_de, keepers = nopower_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_nopower_table-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'hs_nopower_sva_de' not found
## extra_contrasts = extra)
hs_nopower_sva_sig <- extract_significant_genes(
  hs_nopower_sva_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_nopower_sva_sig-v{ver}.xlsx"))
## Error: object 'hs_nopower_sva_table' not found
d01d02_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d01_zymo"]],
                              hs_nopower_sva_table[["data"]][["d02_zymo"]],
                              by = "row.names")
## Error: object 'hs_nopower_sva_table' not found
d0102_zymo_sva_plot <- plot_linear_scatter(d01d02_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd01d02_zymo_sva_comp' not found
d0102_zymo_sva_plot[["scatter"]]
## Error: object 'd0102_zymo_sva_plot' not found
d0102_zymo_sva_plot[["correlation"]]
## Error: object 'd0102_zymo_sva_plot' not found
d0102_zymo_sva_plot[["lm_rsq"]]
## Error: object 'd0102_zymo_sva_plot' not found
d09d81_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d09_zymo"]],
                              hs_nopower_sva_table[["data"]][["d81_zymo"]],
                              by = "row.names")
## Error: object 'hs_nopower_sva_table' not found
d0981_zymo_sva_plot <- plot_linear_scatter(d09d81_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd09d81_zymo_sva_comp' not found
d0981_zymo_sva_plot[["scatter"]]
## Error: object 'd0981_zymo_sva_plot' not found
d0981_zymo_sva_plot[["correlation"]]
## Error: object 'd0981_zymo_sva_plot' not found
d0981_zymo_sva_plot[["lm_rsq"]]
## Error: object 'd0981_zymo_sva_plot' not found
d01d81_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d01_zymo"]],
                              hs_nopower_sva_table[["data"]][["d81_zymo"]],
                              by = "row.names")
## Error: object 'hs_nopower_sva_table' not found
d0181_zymo_sva_plot <- plot_linear_scatter(d01d81_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'd01d81_zymo_sva_comp' not found
d0181_zymo_sva_plot[["scatter"]]
## Error: object 'd0181_zymo_sva_plot' not found
d0181_zymo_sva_plot[["correlation"]]
## Error: object 'd0181_zymo_sva_plot' not found
d0181_zymo_sva_plot[["lm_rsq"]]
## Error: object 'd0181_zymo_sva_plot' not found
upset_plots_sva <- upsetr_sig(hs_nopower_sva_sig, both = TRUE,
                              contrasts = c("d01_zymo", "d02_zymo", "d09_zymo", "d81_zymo"))
## Error: object 'hs_nopower_sva_sig' not found
upset_plots_sva[["up"]]
## Error: object 'upset_plots_sva' not found
upset_plots_sva[["down"]]
## Error: object 'upset_plots_sva' not found
upset_plots_sva[["both"]]
## Error: object 'upset_plots_sva' not found
## The 7th element in the both groups list is the set shared among all donors.
## I don't feel like writing out x:y:z:a
groups <- upset_plots_sva[["both_groups"]]
## Error: object 'upset_plots_sva' not found
shared_genes <- attr(groups, "elements")[groups[[7]]] %>%
  gsub(pattern = "^gene:", replacement = "")
## Error: object 'groups' not found
shared_gp <- simple_gprofiler(shared_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'shared_genes' not found
shared_gp[["pvalue_plots"]][["MF"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["BP"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["REAC"]]
## Error: object 'shared_gp' not found
shared_gp[["pvalue_plots"]][["WP"]]
## Error: object 'shared_gp' not found

10 Donor comparison

Now compare the donors to each other directly.

hs_donors <- set_expt_conditions(hs_macr, fact = "donor")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
donor_de <- all_pairwise(hs_donors, model_svs = "svaseq", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_donors' not found
donor_de
## Error: object 'donor_de' not found
donor_table <- combine_de_tables(
  donor_de,
  excel = glue("analyses/macrophage_de/de_tables/donor_tables-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'donor_de' not found
donor_table
## Error: object 'donor_table' not found
donor_sig <- extract_significant_genes(
  donor_table,
  excel = glue("analyses/macrophage_de/sig_tables/donor_sig-v{ver}.xlsx"))
## Error: object 'donor_table' not found
donor_sig
## Error: object 'donor_sig' not found

11 Primary query contrasts

The final contrast in this list is interesting because it depends on the extra contrasts applied to the all_pairwise() above. In my way of thinking, the primary comparisons to consider are either cross-drug or cross-strain, but not both. However I think in at least a few instances Olga is interested in strain+drug / uninfected+nodrug.

11.1 Write contrast results

Now let us write out the xlsx file containing the above contrasts. The file with the suffix _table-version will therefore contain all genes and the file with the suffix _sig-version will contain only those deemed significant via our default criteria of DESeq2 |logFC| >= 1.0 and adjusted p-value <= 0.05.

11.2 Over representation searches

I decided to make one initially small, but I think quickly big change to the organization of this document: I am moving the GSEA searches up to immediately after the DE. I will then move the plots of the gprofiler results to immediately after the various volcano plots so that it is easier to interpret them.

I am reasonably certain this is the place to check that z23no drug / uninfected has the expected set of genes and that there is or is not a reactome result.

Reproducibility note: Given that this is entirely dependent on an online service, I must assume that the results will change over time; in addition their web servers undergo maintenance regularly, which may result in systematic failure of these analyses. I like gProfiler quite a lot for this type of stuff, but this is an important caveat.

Conversely, the clusterProfiler results later depend on a consistent orgdb annotation set (or reactome or whatever); those versions are fixed by the container installation.

all_gp <- all_gprofiler(hs_macr_sig, enrich_id_column = "hgncsymbol")
## Error: object 'hs_macr_sig' not found
for (g in seq_len(length(all_gp))) {
  name <- names(all_gp)[g]
  datum <- all_gp[[name]]
  filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler-v{ver}.xlsx")
  written <- sm(write_gprofiler_data(datum, excel = filename))
}
## Error: object 'all_gp' not found

11.3 Explicit GSEA search vis clusterProfiler

all_cp <- all_cprofiler(hs_macr_sig, hs_macr_table)
## Error: object 'hs_macr_sig' not found

11.4 Specific desires in Reactome results

In previous analyses (I think by Dr. Colmenares), a specific Tryptophan biosynthesis pathway was observed. Partciularly in the 2.3/uninfected comparison. I think my gprofiler analysis is too stringent and therefore not observing this. Olga asked if I could look at that and see if there are trivial settings I can change to highlight this pathway. The two most likely things I can change are the stringencies of the DE analysis and/or gProfiler.

test_z23_uninf_up <- hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]]
## Error: object 'hs_macr_sig' not found
nrow(test_z23_uninf_up)
## Error: object 'test_z23_uninf_up' not found
test_z23_uninf_down <- hs_macr_sig[["deseq"]][["downs"]][["z23nosb_vs_uninf"]]
## Error: object 'hs_macr_sig' not found
nrow(test_z23_uninf_down)
## Error: object 'test_z23_uninf_down' not found
test_gp_up <- simple_gprofiler(test_z23_uninf_up, enrich_id_column = "hgncsymbol",
                               threshold = 1.0)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'test_z23_uninf_up' not found
test_gp_up
## Error: object 'test_gp_up' not found
written_up <- write_gprofiler_data(test_gp_up, excel = "excel/z23_uninf_gp_up_all.xlsx")
## Error: object 'test_gp_up' not found
test_gp_down <- simple_gprofiler(test_z23_uninf_down, enrich_id_column = "hgncsymbol",
                                 threshold = 1.0)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'test_z23_uninf_down' not found
test_gp_down
## Error: object 'test_gp_down' not found
written_down <- write_gprofiler_data(test_gp_down, excel = "excel/z23_uninf_gp_down_all.xlsx")
## Error: object 'test_gp_down' not found

11.5 Plot contrasts of interest

One suggestion I received recently was to set the axes for these volcano plots to be static rather than let ggplot choose its own. I am assuming this is only relevant for pairs of contrasts, but that might not be true.

11.6 Individual zymodemes vs. uninfected

The following blocks will be a lot of repetition. In each case I am yanking out the volcano plot for a specific contrast and showing the original followed by a version with different colors/labelling.

11.6.1 Infected with z2.3 no Antimonial vs. Uninfected

plot_colors <- get_expt_colors(hs_macr_table[["input"]][["input"]])
## Error in get_expt_colors(hs_macr_table[["input"]][["input"]]): could not find function "get_expt_colors"
## The original plot from my xlsx file
hs_macr_table[["plots"]][["z23nosb_vs_uninf"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23nosb_vs_uninf_volcano <- plot_volcano_condition_de(
  input = hs_macr_table[["data"]][["z23nosb_vs_uninf"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["uninfnone"]], color_high = plot_colors[["infz23"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z23nosb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(limits = c(-6, 21), breaks = c(-6, -4, -2, 0, 2, 4, 6, 8, 10, 20)) +
  ggbreak::scale_x_break(c(10, 19), scales = 0.2, space = 0.02)
## Error: object 'z23nosb_vs_uninf_volcano' not found
pp(file = "figures/fig2a_labeled_with_break.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found
plotly::ggplotly(z23nosb_vs_uninf_volcano[["plot"]])
## Error: object 'z23nosb_vs_uninf_volcano' not found

The following provides some of the over-representation plots from gProfiler2.

all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["KEGG"]]
## Error: object 'all_gp' not found
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
##all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["interactive_plots"]][["WP"]]
## Error: object 'all_gp' not found
message("Olga received a query about the following result, I think it is null.")
## Olga received a query about the following result, I think it is null.
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
message("Is the previous plot null?")
## Is the previous plot null?
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

We have some other categorical enrichment plots available via enrichplot, let us try a few out for contrasts of interest and see if any of them prove helpful.

First, as a reminder, here are the contrasts which are available to examine, in each case there is an _up and _down enrichment object in the data. Thus in the following list I am going to arbitrarily print out some invocations which extract putatively interesting bits of data.

  • z23nosb_vs_uninf: all_gp[[“z23nosb_vs_uninf_up”]][[“BP_enrich”]]
  • z22nosb_vs_uninf.
  • z23nosb_vs_z22nosb.
  • z23sb_vs_z22sb.
  • z23sb_vs_z23nosb.
  • z22sb_vs_z22nosb.
  • z23sb_vs_sb.
  • z22sb_vs_sb.
  • z23sb_vs_uninf.
  • z22sb_vs_uninf.
  • sb_vs_uninf.
  • extra_z2322.
  • extra_drugnodrug.
z23nosb_uninf_up_go <- all_gp[["z23nosb_vs_uninf_up"]][["BP_enrich"]]
## Error: object 'all_gp' not found
z23nosb_uninf_up_go_pair <- pairwise_termsim(z23nosb_uninf_up_go)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'pairwise_termsim': object 'z23nosb_uninf_up_go' not found
dotplot(z23nosb_uninf_up_go)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'dotplot': object 'z23nosb_uninf_up_go' not found
emapplot(z23nosb_uninf_up_go_pair)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'emapplot': object 'z23nosb_uninf_up_go_pair' not found
##ssplot(z23nosb_uninf_up_go_pair)
treeplot(z23nosb_uninf_up_go_pair)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'treeplot': object 'z23nosb_uninf_up_go_pair' not found
upsetplot(z23nosb_uninf_up_go)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'upsetplot': object 'z23nosb_uninf_up_go' not found
cnetplot(z23nosb_uninf_up_go)
## Error: object 'z23nosb_uninf_up_go' not found

11.6.2 Repeat, but using a less strict set of ‘significant genes’

I am not entirely certain if the Reactome results Olga showed me included both up and down genes? I am going to assume for the moment that it was just up/down, but if that proves intractable I will go back to the manuscript and read more carefully (e.g. I just remembered where the picture came from!)

11.6.2.1 Add a little topgo

In the process of exploring the various parameters used with gProfiler2, I found myself thinking that it would be nice to have some topgo results to compare against. The following block is the result of that thought.

test_genes_up <- hs_macr_lesssig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]]
## Error: object 'hs_macr_lesssig' not found
test_query_up <- simple_gprofiler(test_genes_up, threshold = 0.1)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'test_genes_up' not found
test_query_up[["pvalue_plots"]][["REAC"]]
## Error: object 'test_query_up' not found
pdf(file = "images/test_query_biological_process_z23_vs_uninf_up.pdf", height = 12, width = 9)
test_query_up[["pvalue_plots"]][["BP"]]
## Error: object 'test_query_up' not found
dev.off()
## png 
##   2
enrichplot::dotplot(test_query_up[["BP_enrich"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'dotplot': object 'test_query_up' not found
test_genes_down <- hs_macr_lesssig[["deseq"]][["downs"]][["z23nosb_vs_uninf"]]
## Error: object 'hs_macr_lesssig' not found
test_query_down <- simple_gprofiler(test_genes_down)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'test_genes_down' not found
test_query_down[["pvalue_plots"]][["REAC"]]
## Error: object 'test_query_down' not found
## I keep getting all sorts of annoying biomart errors.
hs_go <- try(load_biomart_go(archive = FALSE, overwrite = TRUE))
## Using mart: ENSEMBL_MART_ENSEMBL from host: useast.ensembl.org.
## Unable to perform useDataset, the given dataset is incorrect: hsapiens_gene_ensembl.
## Error in req_perform(request) : Failed to perform HTTP request.
## Caused by error in `curl::curl_fetch_memory()` at httr2/R/req-perform.R:201:5:
## ! Timeout was reached [useast.ensembl.org]:
## Operation timed out after 60002 milliseconds with 0 bytes received
if ("try-error" %in% class(hs_go)) {
  hs_go <- load_biomart_go(archive = TRUE, month = "04", year = "2020", overwrite = TRUE)
}
## Using mart: ENSEMBL_MART_ENSEMBL from host: apr2020.archive.ensembl.org.
## Successfully connected to the hsapiens_gene_ensembl database.
## Warning in gzfile(file, "rb"): cannot open compressed file
## '/home/trey/.cache/biomaRt/file396e4b65cfbd82', probable reason 'No such file
## or directory'
## Finished downloading ensembl go annotations, saving to hsapiens_go_annotations.rda.
## Saving ontologies to hsapiens_go_annotations.rda.
## Finished save().
test_topgo_up <- simple_topgo(test_genes_up, go_db = hs_go[["go"]], parallel = FALSE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'sig_genes' in selecting a method for function 'simple_topgo': object 'test_genes_up' not found
written_topgo <- write_topgo_data(
  test_topgo_up,
  excel = glue("analyses/macrophage_de/ontology_topgo/topgo_z23_uninf_less_strict.xlsx"))
## Error: object 'test_topgo_up' not found

11.6.3 Infected with z2.2 no Antimonial vs. Uninfected

Here is where things will get most repetitive. In each instance I am creating a couple of volcano plots followed by printing some of the gProfiler2 results (when I get the itch).

The following should be a slightly improved version of our extant figure 2B.

## The original plot
hs_macr_table[["plots"]][["z22nosb_vs_uninf"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z22nosb_vs_uninf_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22nosb_vs_uninf"]], "z22nosb_vs_uninf",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["uninfnone"]], color_high = plot_colors[["infz22"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z22nosb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(limits = c(-2, 21), breaks = c(-2, 0, 2, 4, 6, 8, 10, 21, 22)) +
  ggbreak::scale_x_break(c(11, 20), scales = 0.2, space = 0.02)
## Error: object 'z22nosb_vs_uninf_volcano' not found
pp(file = "figures/fig2b_labeled_with_break.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found
plotly::ggplotly(z22nosb_vs_uninf_volcano[["plot"]])
## Error: object 'z22nosb_vs_uninf_volcano' not found

Add some pvalue barplots from gProfiler for this contrast.

all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.2 without drug vs. uninfected without drug, up.

all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.2 without drug vs. uninfected without drug, down.
all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.2 without drug vs. uninfected without drug, down.
all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

11.6.4 Infected with z2.3 treated vs. Uninfected treated

I do not think this plot is used at this time.

## The original plot
hs_macr_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23sb_vs_uninfsb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_sb"]], "z23sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["infsbz23"]], color_high = plot_colors[["uninfsbnone"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
z23sb_vs_uninfsb_volcano[["plot"]]
## Error: object 'z23sb_vs_uninfsb_volcano' not found
plotly::ggplotly(z23sb_vs_uninfsb_volcano[["plot"]])
## Error: object 'z23sb_vs_uninfsb_volcano' not found

11.6.5 Infected with z2.3 untreated vs. z2.2 untreated

This is figure 2C at this time.

## The original plot
hs_macr_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23nosb_vs_z22nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]], "z23nosb_vs_z22nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["infz23"]], color_high = plot_colors[["infz22"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z23nosb_vs_z22nosb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-10, -8, -6, -4, -2, 0, 2, 4, 6))
## Error: object 'z23nosb_vs_z22nosb_volcano' not found
pp(file = "figures/fig2c_labeled.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found

11.6.6 Infected with z2.3 treated vs. z2.2 treated

This is currently figure 3C.

FIXME: The axis label isn’t quite right for the ggbreak.

## The original plot
hs_macr_table[["plots"]][["z23sb_vs_z22sb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23sb_vs_z22sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_z22sb"]], "z23sb_vs_z22sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz23"]], color_low = plot_colors[["infsbz22"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z23sb_vs_z22sb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-23, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-5, -22.5), scales = 10, space = 0.02)
## Error: object 'z23sb_vs_z22sb_volcano' not found
pp(file = "figures/fig3c_labeled_breaks.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found

11.6.7 Infected with z2.3 SB treated vs. z2.3 untreated

I think this is currently figure 3A.

FIXME: The axis label for the ggbreak isn’t quite right.

## The original plot
hs_macr_table[["plots"]][["z23sb_vs_z23nosb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23sb_vs_z23nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_z23nosb"]], "z23sb_vs_z23nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz23"]], color_low = plot_colors[["infz23"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z23sb_vs_z23nosb_volcano[["plot"]] +
  scale_x_continuous(limits = c(-19, 6),
                     breaks = c(-20, -18, -16, -14, -12, -10, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-17, -8), scales = 17, space = 0.02)
## Error: object 'z23sb_vs_z23nosb_volcano' not found
pp(file = "figures/fig3a_labeled_with_break.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found

11.6.8 Infected with z2.3 SB treated vs. z2.3 untreated

## The original plot
hs_macr_table[["plots"]][["z22sb_vs_z22nosb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z22sb_vs_z22nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22sb_vs_z22nosb"]], "z22sb_vs_z22nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz22"]], color_low = plot_colors[["infz22"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- z22sb_vs_z22nosb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-6, -4, -2, 0, 2, 4, 6))
## Error: object 'z22sb_vs_z22nosb_volcano' not found
pp(file = "figures/fig3b_labeled.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found

11.6.9 Infected with z2.3 SB treated vs. uninfected treated

x_limits <- c(-6, 6)
## The original plot
hs_macr_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z23sb_vs_sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_sb"]], "z23sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol", invert = TRUE,
  color_low = plot_colors[["infsbz23"]], color_high = plot_colors[["uninfsbnone"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
z23sb_vs_sb_volcano[["plot"]]
## Error: object 'z23sb_vs_sb_volcano' not found

11.6.10 Infected with z2.2 SB treated vs. uninfected treated

## The original plot
hs_macr_table[["plots"]][["z22sb_vs_sb"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
z22sb_vs_sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22sb_vs_sb"]], "z22sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol", invert = TRUE,
  color_low = plot_colors[["infsbz22"]], color_high = plot_colors[["uninfsbnone"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
z22sb_vs_sb_volcano[["plot"]]
## Error: object 'z22sb_vs_sb_volcano' not found

11.6.11 Uninfected+SbV vs. Uninfected-SbV

This is currently figure 3D.

FIXME: This needs the BOLA2B ggbreak.

## The original plot
hs_macr_table[["plots"]][["sb_vs_uninf"]][["deseq_vol_plots"]]
## Error: object 'hs_macr_table' not found
sb_vs_uninf_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["sb_vs_uninf"]], "sb_vs_uninf",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["uninfsbnone"]], color_low = plot_colors[["uninfnone"]])
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'hs_macr_table' not found
labeled <- sb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-23, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-5, -22.5), scales = 10, space = 0.02)
## Error: object 'sb_vs_uninf_volcano' not found
pp(file = "figures/fig3d_labeled_breaks.svg")
labeled
## Error: object 'labeled' not found
dev.off()
## png 
##   2
labeled
## Error: object 'labeled' not found

11.7 Double-check that gene counts match my perceptions

Check that my perception of the number of significant up/down genes matches what the table/venn says. In the following block I am performing some venn/upset analyses to see if the numbers of genes match what we have in the current version of the manuscript (plus or minus a gene) and thus if my interpretation of the figure/legend text matches what I think it means.

shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_uninf"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/z23_vs_uninf_venn_up.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
## I see 910 z23sb/uninf and 670 no z23nosb/uninf genes in the venn diagram.
length(shared@IntersectionSets[["10"]]) + length(shared@IntersectionSets[["11"]])
## Error: object 'shared' not found
dim(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_uninf"]])
## Error: object 'hs_macr_sig' not found
shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_uninf"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22nosb_vs_uninf"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/z22_vs_uninf_venn_up.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
length(shared@IntersectionSets[["10"]]) + length(shared@IntersectionSets[["11"]])
## Error: object 'shared' not found
dim(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_uninf"]])
## Error: object 'hs_macr_sig' not found

Note to self: There is an error in my volcano plot code which takes effect when the numerator and denominator of the all_pairwise contrasts are different than those in combine_de_tables. It is putting the ups/downs on the correct sides of the plot, but calling the down genes ‘up’ and vice-versa. The reason for this is that I did a check for this happening, but used the wrong argument to handle it.

A likely bit of text for these volcano plots:

The set of genes differentially expressed between the zymodeme 2.3 and uninfected samples without druge treatment was quantified with DESeq2 and included surrogate estimates from SVA. Given the criteria of significance of a abs(logFC) >= 1.0 and false discovery rate adjusted p-value <= 0.05, 670 genes were observed as significantly increased between the infected and uninfected samples and 386 were observed as decreased. The most increased genes from the uninfected samples include some which are potentially indicative of a strong innate immune response and the inflammatory response.

In contrast, when the set of genes differentially expressed between the zymodeme 2.2 and uninfected samples was visualized, only 7 genes were observed as decreased and 435 increased. The inflammatory response was significantly less apparent in this set, but instead included genes related to transporter activity and oxidoreductases.

11.8 Direct zymodeme comparisons

An orthogonal comparison to that performed above is to directly compare the zymodeme 2.3 and 2.2 samples with and without antimonial treatment.

11.8.1 Z2.3 / z2.2 without drug

z23nosb_vs_z22nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## Error in plot_volcano_de(table = hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]], : could not find function "plot_volcano_de"
plotly::ggplotly(z23nosb_vs_z22nosb_volcano[["plot"]])
## Error: object 'z23nosb_vs_z22nosb_volcano' not found
z23sb_vs_z22sb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23sb_vs_z22sb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## Error in plot_volcano_de(table = hs_macr_table[["data"]][["z23sb_vs_z22sb"]], : could not find function "plot_volcano_de"
plotly::ggplotly(z23sb_vs_z22sb_volcano[["plot"]])
## Error: object 'z23sb_vs_z22sb_volcano' not found
z23nosb_vs_z22nosb_volcano[["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)
## Error: object 'z23nosb_vs_z22nosb_volcano' not found
pp(file = "images/z23nosb_vs_z22nosb_reactome_up.svg",
   image = all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
## Error: object 'all_gp' not found
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["KEGG"]]
## Error: object 'all_gp' not found
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["interactive_plots"]][["WP"]]
## Error: object 'all_gp' not found
pp(file = "images/z23nosb_vs_z22nosb_reactome_down.svg",
   image = all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
## Error: object 'all_gp' not found
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

11.8.2 z2.3 / z2.2 with drug

z23sb_vs_z22sb_volcano[["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)
## Error: object 'z23sb_vs_z22sb_volcano' not found
pp(
  file = "images/z23sb_vs_z22sb_reactome_up.png",
  image = all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["REAC"]],
  height = 12, width = 9)
## Error: object 'all_gp' not found
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["KEGG"]]
## Error: object 'all_gp' not found
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["interactive_plots"]][["WP"]]
## Error: object 'all_gp' not found
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

11.8.3 Venn to see shared/unique genes

Once again I wish to pull out the significant genes and see how my numbers match against the text.

shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_z22sb"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_z22nosb"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/drug_nodrug_venn_up.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
shared <- Vennerable::Venn(
  list("drug" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23sb_vs_z22sb"]]),
       "nodrug" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23nosb_vs_z22nosb"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/drug_nodrug_venn_down.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found

A slightly different way of looking at the differences between the two zymodeme infections is to directly compare the infected samples with and without drug. Thus, when a volcano plot showing the comparison of the zymodeme 2.3 vs. 2.2 samples was plotted, 484 genes were observed as increased and 422 decreased; these groups include many of the same inflammatory (up) and membrane (down) genes.

Similar patterns were observed when the antimonial was included. Thus, when a Venn diagram of the two sets of increased genes was plotted, a significant number of the genes was observed as increased (313) and decreased (244) in both the untreated and antimonial treated samples.

11.9 Drug effects on each zymodeme infection

Another likely question is to directly compare the treated vs untreated samples for each zymodeme infection in order to visualize the effects of antimonial.

11.9.1 z2.3 with and without drug

z23sb_vs_z23nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23sb_vs_z23nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## Error in plot_volcano_de(table = hs_macr_table[["data"]][["z23sb_vs_z23nosb"]], : could not find function "plot_volcano_de"
plotly::ggplotly(z23sb_vs_z23nosb_volcano[["plot"]])
## Error: object 'z23sb_vs_z23nosb_volcano' not found
z22sb_vs_z22nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z22sb_vs_z22nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## Error in plot_volcano_de(table = hs_macr_table[["data"]][["z22sb_vs_z22nosb"]], : could not find function "plot_volcano_de"
plotly::ggplotly(z22sb_vs_z22nosb_volcano[["plot"]])
## Error: object 'z22sb_vs_z22nosb_volcano' not found
z23sb_vs_z23nosb_volcano[["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)
## Error: object 'z23sb_vs_z23nosb_volcano' not found
pp(file = "images/z23sb_vs_z23nosb_reactome_up.png",
   image = all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
## Error: object 'all_gp' not found
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["KEGG"]]
## Error: object 'all_gp' not found
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["interactive_plots"]][["WP"]]
## Error: object 'all_gp' not found
all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

11.9.2 z2.2 with and without drug

z22sb_vs_z22nosb_volcano[["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)
## Error: object 'z22sb_vs_z22nosb_volcano' not found
pp(file = "images/z22sb_vs_z22nosb_reactome_up.png",
   image = all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
## Error: object 'all_gp' not found
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["KEGG"]]
## Error: object 'all_gp' not found
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["WP"]]
## Error: object 'all_gp' not found
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["interactive_plots"]][["WP"]]
## Error: object 'all_gp' not found
all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]]
## Error: object 'all_gp' not found
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["MF"]]
## Error: object 'all_gp' not found
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["TF"]]
## Error: object 'all_gp' not found
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.

11.9.3 Shared and unique genes after/before drug

shared <- Vennerable::Venn(list(
  "z23" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_z23nosb"]]),
  "z22" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_z22nosb"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/z23_z22_drug_venn_up.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
shared <- Vennerable::Venn(list(
  "z23" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23sb_vs_z23nosb"]]),
  "z22" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z22sb_vs_z22nosb"]])))
## Error: object 'hs_macr_sig' not found
pp(file = "images/z23_z22_drug_venn_down.png")
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found
dev.off()
## png 
##   2
Vennerable::plot(shared)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'shared' not found

Note: I am settig the x and y-axis boundaries by allowing the plotter to pick its own axis the first time, writing down the ranges I observe, and then setting them to the largest of the pair. It is therefore possible that I missed one or more genes which lies outside that range.

The previous plotted contrasts sought to show changes between the two strains z2.3 and z2.2. Conversely, the previous volcano plots seek to directly compare each strain before/after drug treatment.

12 LRT of the Human Macrophage

A slightly different tack to examine the data is to perform a likelihood ratio test in order to look for trends which are shared among genes when examining different conditions in the data.

tmrc2_lrt_strain_drug <- deseq_lrt(hs_macr, interactor_column = "drug",
                                   interest_column = "macrophagezymodeme",
                                   factors = c("drug", "macrophagezymodeme"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'hs_macr' not found
tmrc2_lrt_strain_drug[["cluster_data"]][["plot"]]
## Error: object 'tmrc2_lrt_strain_drug' not found

13 Parasite

Let us consider for a moment differences among the parasite transcriptomes for the samples which were not drug treated.

One thing I did in the initial implementation of this document was to repeat the variable ‘up_genes’ for each comparison; I think this time I will make a different variable for each comparison so I can play with them a little further.

comparison <- "z23_vs_z22"
lp_macrophage_de <- all_pairwise(lp_macrophage_nosb,
                                 model_svs = "svaseq", filter = TRUE)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pData': object 'lp_macrophage_nosb' not found
tmrc2_parasite_keepers <- list(
  "z23_vs_z22" = c("z23", "z22"))
lp_macrophage_table <- combine_de_tables(
  lp_macrophage_de, keepers = tmrc2_parasite_keepers,
  excel = glue("analyses/macrophage_de/de_tables/parasite_infection_de-v{ver}.xlsx"))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'table' in selecting a method for function '%in%': object 'lp_macrophage_de' not found
lp_macrophage_sig <- extract_significant_genes(
  lp_macrophage_table,
  excel = glue("analyses/macrophage_de/sig_tables/parasite_sig-v{ver}.xlsx"))
## Error: object 'lp_macrophage_table' not found
lp_macrophage_table[["plots"]][[comparison]][["deseq_vol_plots"]]
## Error: object 'lp_macrophage_table' not found
lp_macrophage_table[["plots"]][[comparison]][["deseq_ma_plots"]]
## Error: object 'lp_macrophage_table' not found
up_genes_z23z22 <- lp_macrophage_sig[["deseq"]][["ups"]][[comparison]]
## Error: object 'lp_macrophage_sig' not found
dim(up_genes_z23z22)
## Error: object 'up_genes_z23z22' not found
down_genes_z23z22 <- lp_macrophage_sig[["deseq"]][["downs"]][[comparison]]
## Error: object 'lp_macrophage_sig' not found
dim(down_genes_z23z22)
## Error: object 'down_genes_z23z22' not found
lp_z23sb_vs_z22sb_volcano <- plot_volcano_de(
  table = lp_macrophage_table[["data"]][["z23_vs_z22"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## Error in plot_volcano_de(table = lp_macrophage_table[["data"]][["z23_vs_z22"]], : could not find function "plot_volcano_de"
plotly::ggplotly(lp_z23sb_vs_z22sb_volcano[["plot"]])
## Error: object 'lp_z23sb_vs_z22sb_volcano' not found
lp_z23sb_vs_z22sb_volcano[["plot"]]
## Error: object 'lp_z23sb_vs_z22sb_volcano' not found

14 GSVA

Note: The following block assumes one is able to download a fresh copy of msigDB, which I am not sure is possible within the constraints of a container (I mean it is trivial to do, but I am not sure if it is ok due to licensing). However, Broad provides a data package of a msigdb release. As a result, the following block will be repeated using that.

hs_infected <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
hs_gsva_c2 <- simple_gsva(hs_infected)
hs_gsva_c2_meta <- get_msigdb_metadata(hs_gsva_c2, msig_xml = "reference/msigdb_v7.2.xml")
hs_gsva_c2_sig <- get_sig_gsva_categories(
  hs_gsva_c2_meta,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c2_sig.xlsx"))
hs_gsva_c2_sig[["raw_plot"]]

hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")
hs_gsva_c7_meta <- get_msigdb_metadata(hs_gsva_c7, msig_xml = "reference/msigdb_v7.2.xml")
hs_gsva_c7_sig <- get_sig_gsva_categories(
  hs_gsva_c7,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c7_sig.xlsx"))
hs_gsva_c7_sig[["raw_plot"]]

14.1 Repeat using the GSVAdata package.

hs_infected <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'hs_macrophage' not found
hs_gsva_c2 <- simple_gsva(hs_infected)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'eset' in selecting a method for function 'simple_gsva': object 'hs_infected' not found
##hs_gsva_c2_meta <- get_msigdb_metadata(hs_gsva_c2, msig_xml="reference/msigdb_v7.2.xml")
hs_gsva_c2_sig <- get_sig_gsva_categories(
  hs_gsva_c2,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c2_sig.xlsx"))
## Error: object 'hs_gsva_c2' not found
hs_gsva_c2_sig[["raw_plot"]]
## Error: object 'hs_gsva_c2_sig' not found
hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'eset' in selecting a method for function 'simple_gsva': object 'hs_infected' not found
##hs_gsva_c7_meta <- get_msigdb_metadata(hs_gsva_c7, msig_xml="reference/msigdb_v7.2.xml")
hs_gsva_c7_sig <- get_sig_gsva_categories(
  hs_gsva_c7,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c7_sig.xlsx"))
## Error: object 'hs_gsva_c7' not found
hs_gsva_c7_sig[["raw_plot"]]
## Error: object 'hs_gsva_c7_sig' not found

15 Try out a new tool

Two reasons: Najib loves him some PCA, this uses wikipathways, which is something I think is neat.

Ok, I spent some time looking through the code and I have some problems with some of the design decisions.

Most importantly, it requires a data.frame() which has the following format:

  1. No rownames, instead column #1 is the sample ID.
  2. Columns 2-m are the categorical/survival/etc metrics.
  3. Columns m-n are 1 gene-per-column with log2 values.

But when I think about it I think I get the idea, they want to be able to do modelling stuff more easily with response factors.

library(pathwayPCA)
library(rWikiPathways)

downloaded <- downloadPathwayArchive(organism = "Homo sapiens", format = "gmt")
data_path <- system.file("extdata", package = "pathwayPCA")
wikipathways <- read_gmt(paste0(data_path, "/wikipathways_human_symbol.gmt"),
                         description = TRUE)

expt <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': error in evaluating the argument 'expt' in selecting a method for function 'subset_expt': object 'hs_macrophage' not found
expt <- set_expt_conditions(expt, fact = "macrophagezymodeme")
## Error: unable to find an inherited method for function 'pData' for signature 'object = "function"'
symbol_column <- "hgnc_symbol"
symbol_vector <- fData(expt)[[symbol_column]]
## Error: unable to find an inherited method for function 'fData' for signature 'object = "function"'
names(symbol_vector) <- rownames(fData(expt))
## Error: unable to find an inherited method for function 'fData' for signature 'object = "function"'
symbol_df <- as.data.frame(symbol_vector)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'symbol_vector' not found
assay_df <- merge(symbol_df, as.data.frame(exprs(expt)), by = "row.names")
## Error: object 'symbol_df' not found
assay_df[["Row.names"]] <- NULL
## Error: object 'assay_df' not found
rownames(assay_df) <- make.names(assay_df[["symbol_vector"]], unique = TRUE)
## Error: object 'assay_df' not found
assay_df[["symbol_vector"]] <- NULL
## Error: object 'assay_df' not found
assay_df <- as.data.frame(t(assay_df))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'assay_df' not found
assay_df[["SampleID"]] <- rownames(assay_df)
## Error: object 'assay_df' not found
assay_df <- dplyr::select(assay_df, "SampleID", everything())
## Error: object 'assay_df' not found
factor_df <- as.data.frame(pData(expt))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': unable to find an inherited method for function 'pData' for signature 'object = "function"'
factor_df[["SampleID"]] <- rownames(factor_df)
## Error: object 'factor_df' not found
factor_df <- dplyr::select(factor_df, "SampleID", everything())
## Error: object 'factor_df' not found
factor_df <- factor_df[, c("SampleID", factors)]
## Error: object 'factor_df' not found
tt <- CreateOmics(
  assayData_df = assay_df,
  pathwayCollection_ls = wikipathways,
  response = factor_df,
  respType = "categorical",
  minPathSize = 5)
## Error: object 'factor_df' not found
super <- AESPCA_pVals(
  object = tt,
  numPCs = 2,
  parallel = FALSE,
  numCores = 8,
  numReps = 2,
  adjustment = "BH")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'AESPCA_pVals': object 'tt' not found

16 Evaluating a log2FC barplot

Figure 2E is now comprised of a plot which shows log2FC values with error bars for selected genes and seeks to show differences between 2.3/uninfected and 2.2/uninfected.

Here is the table Olga used to generate it:

I went looking in the xlsx files produced in 202405 and found that these are the log2FC values and standard errors produced by DESeq2.

It should be noted that in my most recent version of these analyses, these numbers did shift slightly. I am looking into that now.

  • Data witout drug

** 2.3 vs Uninfected MØ 2.2 vs Uninfected MØ

16.1 | Gene | Mean | SEM | n | Mean | SEM |n |

|IFI27 | 7.224 | 0.5662 |6 | 2.702 | 0.5669 | 6| |RSAD2 | 6.29 | 0.7312 |6 | 1.623 | 0.7303 | 6| |CCL8 | 6.225 | 0.928 |6 | -0.314| 0.941 | 6| |IFI44L| 5.895 | 0.612 |6 | 2.06 | 0.611 | 6| |OASL | 4.726 | 0.4974 |6 | 1.392 | 0.4973 | 6| |USP18 | 3.644 | 0.483 |6 | 0.999 | 0.4826 | 6| |IDO1 | 7.145 | 1.107 |6 | 1.257 | 1.141 | 6| |IDO2 | 3.935 | 1.3 |6 | 2.557 | 1.341 | 6| |KYNU | 1.07 | 0.2186 |6 | 0.0207| 0.2184 | 6| |AHR | 0.9382 | 0.2236 |6 | 0.5032| 0.2239 | 6| |IL4I1 | 2.593 | 0.4623 |6 | 0.039 | 0.4618 | 6| |SOD2 | 2.76 | 0.349 |6 | 0.4241| 0.3528 | 6| |NOTCH1| 0.7572| 0.275 |6 | 1.495 | 0.2744 | 6| |DLL1 | 0.8268| 0.5285 |6 | 3.455 | 0.5228 | 6| |DLL4 | 1.116 | 0.737 |6 | 4.243 | 0.71 | 6| |HES1 | -0.0183| 0.8599 |6 | 6.536 | 0.7973 | 6| |HEY1 | 0.5533| 0.5789 |6 | 4.181 | 0.6273 | 6|

Ok, I think I found a problem: The NOTCH1 value is actually the adjusted p-value.

  • Transporters without drug

** 2.3 vs Uninfected MØ 2.2 vs Uninfected MØ

16.2 | Gene | Mean | SEM | n| Mean | SEM | n|

|ABCB1 | -2.354 | 0.442 | 6| -0.406| 0.431| 6| |ABCG4 | -3.715 | 0.648 | 6| -0.653| 0.630| 6| |ABCB5 | -1.192 | 0.380 | 6| 1.351 | 0.363| 6| |ABCA9 | 1.880 | 0.648 | 6| 3.444 | 0.637| 6| |ABCC2 | 0.454 | 0.321 | 6| 1.818 | 0.314| 6| |AQP2 | -1.191 | 0.529 | 6| 0.745 | 0.514| 6| |AQP3 | -0.940 | 0.402 | 6| 0.431 | 0.395| 6|

  • Transporters with drug

** 2.3 vs Uninfected MØ 2.2 vs Uninfected MØ

16.3 |Gene | Mean | SEM | n| Mean | SEM | n |

|ABCB1 | -0.697| 0.349 | 6| -1.255| 0.337 | 6| |ABCG4 | 1.231 | 0.503 | 6| 0.547 | 0.484 | 6| |AQP2 | 0.816 | 0.399 | 6| 0.043 | 0.387 | 6| |AQP3 | -1.286| 0.320 | 6| -1.613| 0.309 | 6| |AQP8 | 0.634 | 0.370 | 6| 0.943 | 0.365 | 6|

Let us now see if I can recapitulate the plot…

nodrug_contrasts <- c("z23nosb_vs_uninf", "z22nosb_vs_uninf")
genes_no_drug <- c("IFI27", "RSAD2", "CCL8", "IFI44L", "OASL", "USP18", "IDO1", "IDO2", "KYNU", "AHR", "IL4I1", "SOD2", "NOTCH1", "DLL1", "DLL4", "HES1", "HEY1")
transporters_no_drug <- c("ABCB1", "ABCG4", "ABCB5", "ABCA9", "ABCC2", "AQP2", "AQP3")
drug_contrasts <- c("z23sb_vs_sb", "z22sb_vs_sb")
transporters_drug <- c("ABCB1", "ABCG4", "AQP2", "AQP3", "AQP8")

These values came out of the data structure called ‘hs_macr_table’

z23nosb_uninf_values <- hs_macr_table[["data"]][["z23nosb_vs_uninf"]]
## Error: object 'hs_macr_table' not found
gene_idx <- z23nosb_uninf_values[["hgnc_symbol"]] %in% genes_no_drug
## Error: object 'z23nosb_uninf_values' not found
nodrug_rows <-  z23nosb_uninf_values[gene_idx, ]
## Error: object 'z23nosb_uninf_values' not found
rownames(nodrug_rows) <- nodrug_rows[["hgnc_symbol"]]
## Error: object 'nodrug_rows' not found
z23_nodrug_values <- nodrug_rows[, c("deseq_logfc", "deseq_lfcse")]
## Error: object 'nodrug_rows' not found
z23_nodrug_values
## Error: object 'z23_nodrug_values' not found
z22nosb_uninf_values <- hs_macr_table[["data"]][["z22nosb_vs_uninf"]]
## Error: object 'hs_macr_table' not found
gene_idx <- z22nosb_uninf_values[["hgnc_symbol"]] %in% genes_no_drug
## Error: object 'z22nosb_uninf_values' not found
nodrug_rows <-  z22nosb_uninf_values[gene_idx, ]
## Error: object 'z22nosb_uninf_values' not found
rownames(nodrug_rows) <- nodrug_rows[["hgnc_symbol"]]
## Error: object 'nodrug_rows' not found
z22_nodrug_values <- nodrug_rows[, c("deseq_logfc", "deseq_lfcse")]
## Error: object 'nodrug_rows' not found
z22_nodrug_values
## Error: object 'z22_nodrug_values' not found
z23_nodrug_values[["state"]] <- "z23_vs_uninfected"
## Error: object 'z23_nodrug_values' not found
z22_nodrug_values[["state"]] <- "z22_vs_uninfected"
## Error: object 'z22_nodrug_values' not found
plot_df <- rbind.data.frame(as.data.frame(z23_nodrug_values), as.data.frame(z22_nodrug_values))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'z23_nodrug_values' not found
plot_df[["gene"]] <- rownames(plot_df)
## Error: object 'plot_df' not found
## I just realized that this is actually just a comparison of z23/z22
## we should just take the adjusted p-values from that contrast for this.
z23_z22_comparison <- hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]]
## Error: object 'hs_macr_table' not found
nodrug_rows <- z23_z22_comparison[gene_idx, ]
## Error: object 'z23_z22_comparison' not found
nodrug_pvalues <- nodrug_rows[, c("deseq_p", "deseq_adjp")]
## Error: object 'nodrug_rows' not found
rownames(nodrug_pvalues) <- nodrug_rows[["hgnc_symbol"]]
## Error: object 'nodrug_rows' not found
nodrug_pvalues
## Error: object 'nodrug_pvalues' not found
ggplot(plot_df, aes(x = gene, y = deseq_logfc, fill = state)) +
  geom_bar(position = position_dodge(), stat = "identity") +
  geom_errorbar(aes(ymin = deseq_logfc - deseq_lfcse,
                    ymax = deseq_logfc + deseq_lfcse),
                width = 0.2, position = position_dodge(0.9)) +
  scale_fill_manual(values = c("#1B9E77", "#7570B3")) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
## Error: object 'plot_df' not found
comparison <- c("z23_vs_uninfected", "z22_vs_uninfected")
comparisons <- rep(list(comparison), nrow(plot_df) / 2)
## Error: object 'plot_df' not found
ggplot(plot_df, aes(x = gene, y = deseq_logfc, fill = state, add = deseq_lfcse, facet.by = "state")) +
  geom_bar(position = position_dodge(), stat = "identity") +
  geom_errorbar(aes(ymin = deseq_logfc - deseq_lfcse,
                    ymax = deseq_logfc + deseq_lfcse),
                width = 0.2, position = position_dodge(0.9)) +
  stat_compare_means() +
  stat_compare_means(comparisons = comparisons, label.y = rownames(z23_nodrug_values)) +
  scale_fill_manual(values = c("#1B9E77", "#7570B3")) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
## Error: object 'plot_df' not found

Excellent, the values now match up. Now I ust need to figure out why the stupid hgnc IDs got lost… I can see them in the hs_annot data structure, so I must have messed up when I regenered the input to the de. Ok, I got to the same starting point now with identical values. As soon as I did that, I looked at the resulting plot and realized that we are actually just comparing z23 / z22.

Here is why: the plot as it stands is a comparison of the log2FC values of the following two contrasts: z23/uninfected and z22/uninfected; stated differently, this is (z23/uninf)/(z22/uninf) which of course cancels out to just z23/z22.

Therefore it is much more parsimonious to just use the values from z23/z22. I swear I have gone through this exact exercise on so so many occasions in the past it is terrible.

wanted_genes <- c("IFI27", "RSAD2", "CCL8", "IFI44L", "OASL",
                  "USP18", "IDO1", "IDO2", "KYNU", "AHR", "IL4I1",
                  "SOD2", "NOTCH1", "DLL1", "DLL4", "HES1", "HEY1")
ggsignif_plot <- ggsignif_paired_genes(
  hs_macr, conditions = c("inf_z23", "inf_z22"), genes = wanted_genes)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'normalize': object 'hs_macr' not found
ggsignif_plot
## Error: object 'ggsignif_plot' not found
pander::pander(sessionInfo())
## Warning: Your system is mis-configured: '/etc/localtime' is not a symlink
## Warning: It is strongly recommended to set envionment variable TZ to
## 'America/New_York' (or equivalent)

R version 4.5.0 (2025-04-11)

Platform: x86_64-pc-linux-gnu

locale: C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: rWikiPathways(v.1.28.0), pathwayPCA(v.1.24.0), ggstatsplot(v.0.13.1), enrichplot(v.1.28.4), tidyr(v.1.3.1), tibble(v.3.3.0), UpSetR(v.1.4.0), hpgltools(v.1.2), Heatplus(v.3.16.0), glue(v.1.8.0), ggplot2(v.3.5.2) and ggbreak(v.0.1.6)

loaded via a namespace (and not attached): splines(v.4.5.0), later(v.1.4.3), bitops(v.1.0-9), ggplotify(v.0.1.2), filelock(v.1.0.3), R.oo(v.1.27.1), datawizard(v.1.2.0), graph(v.1.86.0), XML(v.3.99-0.19), lifecycle(v.1.0.4), httr2(v.1.2.1), edgeR(v.4.6.3), lattice(v.0.22-7), insight(v.1.4.0), backports(v.1.5.0), magrittr(v.2.0.3), openxlsx(v.4.2.8), limma(v.3.64.3), plotly(v.4.11.0), sass(v.0.4.10), rmarkdown(v.2.29), jquerylib(v.0.1.4), yaml(v.2.3.10), httpuv(v.1.6.16), ggtangle(v.0.0.7), zip(v.2.3.3), cowplot(v.1.2.0), DBI(v.1.2.3), RColorBrewer(v.1.1-3), abind(v.1.4-8), GenomicRanges(v.1.60.0), purrr(v.1.1.0), R.utils(v.2.13.0), RCurl(v.1.98-1.17), BiocGenerics(v.0.54.0), yulab.utils(v.0.2.1), rappdirs(v.0.3.3), GenomeInfoDbData(v.1.2.14), IRanges(v.2.42.0), S4Vectors(v.0.46.0), ggrepel(v.0.9.6), correlation(v.0.8.8), tidytree(v.0.4.6), Vennerable(v.3.1.0.9000), annotate(v.1.86.1), codetools(v.0.2-20), DelayedArray(v.0.34.1), xml2(v.1.4.0), DOSE(v.4.2.0), tidyselect(v.1.2.1), aplot(v.0.2.8), UCSC.utils(v.1.4.0), farver(v.2.1.2), effectsize(v.1.0.1), matrixStats(v.1.5.0), stats4(v.4.5.0), BiocFileCache(v.2.16.1), jsonlite(v.2.0.0), survival(v.3.8-3), iterators(v.1.0.14), foreach(v.1.5.2), tools(v.4.5.0), progress(v.1.2.3), treeio(v.1.32.0), Rcpp(v.1.1.0), gridExtra(v.2.3), SparseArray(v.1.8.1), xfun(v.0.53), DESeq2(v.1.48.1), qvalue(v.2.40.0), MatrixGenerics(v.1.20.0), GenomeInfoDb(v.1.44.2), dplyr(v.1.1.4), withr(v.3.0.2), BiocManager(v.1.30.26), fastmap(v.1.2.0), digest(v.0.6.37), R6(v.2.6.1), mime(v.0.13), gridGraphics(v.0.5-1), GO.db(v.3.21.0), dichromat(v.2.0-0.1), biomaRt(v.2.64.0), RSQLite(v.2.4.3), R.methodsS3(v.1.8.2), generics(v.0.1.4), data.table(v.1.17.8), prettyunits(v.1.2.0), httr(v.1.4.7), htmlwidgets(v.1.6.4), S4Arrays(v.1.8.1), parameters(v.0.28.0), pkgconfig(v.2.0.3), gtable(v.0.3.6), blob(v.1.2.4), statsExpressions(v.1.7.1), XVector(v.0.48.0), htmltools(v.0.5.8.1), fgsea(v.1.34.2), RBGL(v.1.84.0), GSEABase(v.1.70.0), scales(v.1.4.0), Biobase(v.2.68.0), png(v.0.1-8), ggfun(v.0.2.0), knitr(v.1.50), rjson(v.0.2.23), reshape2(v.1.4.4), nlme(v.3.1-168), curl(v.7.0.0), cachem(v.1.1.0), stringr(v.1.5.1), parallel(v.4.5.0), AnnotationDbi(v.1.70.0), pillar(v.1.11.0), grid(v.4.5.0), vctrs(v.0.6.5), promises(v.1.3.3), dbplyr(v.2.5.0), xtable(v.1.8-4), paletteer(v.1.6.0), evaluate(v.1.0.4), zeallot(v.0.2.0), cli(v.3.6.5), locfit(v.1.5-9.12), compiler(v.4.5.0), rlang(v.1.1.6), crayon(v.1.5.3), rstantools(v.2.4.0), rematch2(v.2.1.2), plyr(v.1.8.9), fs(v.1.6.6), pander(v.0.6.6), stringi(v.1.8.7), viridisLite(v.0.4.2), BiocParallel(v.1.42.1), Biostrings(v.2.76.0), lazyeval(v.0.2.2), bayestestR(v.0.16.1), GOSemSim(v.2.34.0), Matrix(v.1.7-3), lars(v.1.3), hms(v.1.1.3), patchwork(v.1.3.2), bit64(v.4.6.0-1), varhandle(v.2.0.6), KEGGREST(v.1.48.1), statmod(v.1.5.0), shiny(v.1.11.1), SummarizedExperiment(v.1.38.1), igraph(v.2.1.4), broom(v.1.0.9), memoise(v.2.0.1), RcppParallel(v.5.1.11-1), bslib(v.0.9.0), ggtree(v.3.17.1.001), fastmatch(v.1.1-6), bit(v.4.6.0) and ape(v.5.8-1)

message("This is hpgltools commit: ", get_git_commit())
## If you wish to reproduce this exact build of hpgltools, invoke the following:
## > git clone http://github.com/abelew/hpgltools.git
## > git reset 7e330419388a441dce04bfb7cce31bb7c8be3ef4
## This is hpgltools commit: Thu Aug 28 22:20:45 2025 -0400: 7e330419388a441dce04bfb7cce31bb7c8be3ef4
tmp <- saveme(filename = savefile)
## The savefile is: /lab/singularity/tmrc2_macrophage_deb/202508291011_outputs/savefiles/03differential_expression.rda.xz
## The file does not yet exist.
## The save string is: con <- pipe(paste0('pxz > /lab/singularity/tmrc2_macrophage_deb/202508291011_outputs/savefiles/03differential_expression.rda.xz'), 'wb'); save(list = ls(all.names = TRUE, envir = globalenv()),
##      envir = globalenv(), file = con, compress = FALSE); close(con)
## Error in save(list = ls(all.names = TRUE, envir = globalenv()), envir = globalenv(), : ignoring SIGPIPE signal
tmp <- loadme(filename = savefile)

devtools::load_all(‘~/hpgltools’)

---
title: "TMRC2 `r Sys.getenv('VERSION')`: Macrophage Differential Expression."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: zenburn
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
---

<style type="text/css">
body .main-container {
  max-width: 1600px;
}
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
  font-size: 16px
}
</style>

```{r options, include = FALSE}
library(ggbreak)
library(ggplot2)
library(glue)
library(Heatplus)
library(hpgltools)
library(UpSetR)
library(tibble)
library(tidyr)
library(enrichplot)
library(ggstatsplot)

knitr::opts_knit$set(progress = TRUE, verbose = TRUE, width = 90, echo = TRUE)
knitr::opts_chunk$set(
  error = TRUE, fig.width = 8, fig.height = 8, fig.retina = 2,
  out.width = "100%", dev = "png",
  dev.args = list(png = list(type = "cairo-png")))
old_options <- options(digits = 4, stringsAsFactors = FALSE, knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- Sys.getenv("VERSION")
previous_file <- ""
rundate <- format(Sys.Date(), format = "%Y%m%d")

## tmp <- try(sm(loadme(filename = gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = previous_file))))
rmd_file <- "03differential_expression.Rmd"
loaded <- load(file = glue("rda/tmrc2_data_structures-v{ver}.rda"))
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
created <- dir.create("analyses")
```

# Changelog

* 202401-202405: Cleanups, formatting, ensuring that everything works in the container.
* 202310: Cleaning up to make everything pass within a containerized environment.
* 202310: Received a set of colors and contrasts of interest for a barplot of significance.
* 20230410: Making some changes to improve the differential expression
  plots as well as prepare for some different pathway/GSEA/GSVA
  analyses on the data.

# Introduction

Having established that the TMRC2 macrophage data looks robust and
illustrative of a couple of interesting questions, let us perform a
couple of differential analyses of it.

Also note that as of 202212, we received a new set of samples which
now include some which are a completely different cell type,
U937.  As their ATCC page states, they are malignant cells taken from
the pleural effusion of a 37 year old white male with histiocytic
lymphoma and which exhibit the morphology of monocytes.  Thus, this
document now includes some comparisons of the cell types as well as
the various macrophage donors (given that there are now more donors
too).

## Human data

I am moving the dataset manipulations here so that I can look at them
all together before running the various DE analyses.

## Create sets focused on drug, celltype, strain, and combinations

Let us start by playing with the metadata a little and create sets
with the condition set to:

* Drug treatment
* Cell type (macrophage or U937)
* Donor
* Infection Strain
* Some useful combinations thereof

In addition, keep mental track of which datasets are comprised of all
samples vs. those which are only macrophage vs. those which are only
U937.  (Thus, the usage of all_human vs. hs_macr vs. u937 as prefixes
for the data structures.)

Ideally, these recreations of the data should perhaps be in the
datastructures worksheet.

```{r}
all_human <- sanitize_expt_pData(hs_macrophage, columns = "drug") %>%
  set_conditions(fact = "drug", colors = color_choices[["drug"]]) %>%
  set_batches(fact = "typeofcells")

## The following 3 lines were copy/pasted to datastructures and should be removed soon.
no_strain_idx <- colData(all_human)[["strainid"]] == "none"
##pData(all_human)[["strainid"]] <- paste0("s", pData(all_human)[["strainid"]],
##                                         "_", pData(all_human)[["macrophagezymodeme"]])
colData(all_human)[no_strain_idx, "strainid"] <- "none"
table(colData(all_human)[["strainid"]])

all_human_types <- set_conditions(all_human, fact = "typeofcells") %>%
  set_batches(fact = "drug")

type_zymo_fact <- paste0(colData(all_human_types)[["condition"]], "_",
                         colData(all_human_types)[["macrophagezymodeme"]])
type_zymo <- set_conditions(all_human_types, fact = type_zymo_fact)

type_drug_fact <- paste0(colData(all_human_types)[["condition"]], "_",
                         colData(all_human_types)[["drug"]])
type_drug <- set_conditions(all_human_types, fact = type_drug_fact)

strain_fact <- colData(all_human_types)[["strainid"]]
table(strain_fact)

new_conditions <- paste0(colData(hs_macrophage)[["macrophagetreatment"]], "_",
                         colData(hs_macrophage)[["macrophagezymodeme"]])
## Note the sanitize() call is redundant with the addition of sanitize() in the
## datastructures file, but I don't want to wait to rerun that.
hs_macr <- set_conditions(hs_macrophage, fact = new_conditions) %>%
  sanitize_expt_pData(column = "drug") %>%
  subset_expt(subset = "typeofcells!='U937'") %>%
  set_se_colors(color_choices[["treatment_zymo"]])
```

### Separate Macrophage samples

Once again, we should reconsider where the following block is placed,
but these datastructures are likely to be used in many of the
following analyses.

```{r}
hs_macr_drug_expt <- set_conditions(hs_macr, fact = "drug", colors = color_choices[["drug"]])

hs_macr_strain_expt <- set_conditions(hs_macr, fact = "macrophagezymodeme",
                                      colors = color_choices[["zymo"]]) %>%
  subset_se(subset = "macrophagezymodeme != 'none'")

table(colData(hs_macr)[["strainid"]])
```

### Refactor U937 samples

The U937 samples were separated in the datastructures file, but we
want to use the combination of drug/zymodeme with them pretty much
exclusively.

```{r}
new_conditions <- paste0(colData(hs_u937)[["macrophagetreatment"]], "_",
                         colData(hs_u937)[["macrophagezymodeme"]])
u937_expt <- set_conditions(hs_u937, fact = new_conditions,
                            colors = color_choices[["treatment_zymo"]])
```

## Contrasts used in this document

Given the various ways we have chopped up this dataset, there are a
few general types of contrasts we will perform, which will then be
combined into greater complexity:

* drug treatment: Antimonal treated or not.
* strains used: Uninfected, z2.3, and z2.2.
* cellltypes: U937 or macrophage.
* donors: The person from whom the macrophages were taken.

In the end, our actual goal is to consider the variable effects of
drug+strain and see if we can discern patterns which lead to better or
worse drug treatment outcome.

There is a set of contrasts in which we are primarily interested in
this data, these follow.  I created one ratio of ratios contrast which
I think has the potential to ask our biggest question.

```{r}
## Each of the following lists has the name of the contrast as the key
## followed by a two element vector comprised of the numerator and
## denominator as the value.  In the case of this first contrast, that
## is comprised of a string which manually defines a series of more
## complex contrasts than the usual/simple pairwise.
tmrc2_human_extra <- "z23drugnodrug_vs_z22drugnodrug = (conditioninf_sb_z23 - conditioninf_z23) - (conditioninf_sb_z22 - conditioninf_z22), z23z22drug_vs_z23z22nodrug = (conditioninf_sb_z23 - conditioninf_sb_z22) - (conditioninf_z23 - conditioninf_z22)"
tmrc2_human_keepers <- list(
  "z23nosb_vs_uninf" = c("inf_z23", "uninf_none"),
  "z22nosb_vs_uninf" = c("inf_z22", "uninf_none"),
  "z23nosb_vs_z22nosb" = c("inf_z23", "inf_z22"),
  "z23sb_vs_z22sb" = c("inf_sb_z23", "inf_sb_z22"),
  "z23sb_vs_z23nosb" = c("inf_sb_z23", "inf_z23"),
  "z22sb_vs_z22nosb" = c("inf_sb_z22", "inf_z22"),
  "z23sb_vs_sb" = c("inf_sb_z23", "uninf_sb_none"),
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"),
  "z23sb_vs_uninf" = c("inf_sb_z23", "uninf_none"),
  "z22sb_vs_uninf" = c("inf_sb_z22", "uninf_none"),
  "sb_vs_uninf" = c("uninf_sb_none", "uninf_none"),
  "extra_z2322" = c("z23drugnodrug", "z22drugnodrug"),
  "extra_drugnodrug" = c("z23z22drug", "z23z22nodrug"))
single_tmrc2_keeper <- list(
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"))
tmrc2_drug_keepers <- list(
  "drug" = c("antimony", "none"))
tmrc2_type_keepers <- list(
  "type" = c("U937", "Macrophages"))
tmrc2_strain_keepers <- list(
  "strain" = c("z23", "z22"))
type_zymo_extra <- "zymos_vs_types = (conditionU937_z23 - conditionU937_z22) - (conditionMacrophages_z23 - conditionMacrophages_z22)"
tmrc2_typezymo_keepers <- list(
  "u937_macr" = c("Macrophages_none", "U937_none"),
  "zymo_macr" = c("Macrophages_z23", "Macrophages_z22"),
  "zymo_u937" = c("U937_z23", "U937_z22"),
  "z23_types" = c("U937_z23", "Macrophages_z23"),
  "z22_types" = c("U937_z22", "Macrophages_z22"),
  "zymos_types" = c("zymos_vs_types"))
tmrc2_typedrug_keepers <- list(
  "type_nodrug" = c("U937_none", "Macrophages_none"),
  "type_drug" = c("U937_antimony", "Macrophages_antimony"),
  "macr_drugs" = c("Macrophages_antimony", "Macrophages_none"),
  "u937_drugs" = c("U937_antimony", "U937_none"))
u937_keepers <- list(
  "z23nosb_vs_uninf" = c("inf_z23", "uninf_none"),
  "z22nosb_vs_uninf" = c("inf_z22", "uninf_none"),
  "z23nosb_vs_z22nosb" = c("inf_z23", "inf_z22"),
  "z23sb_vs_z22sb" = c("inf_sb_z23", "inf_sb_z22"),
  "z23sb_vs_z23nosb" = c("inf_sb_z23", "inf_z23"),
  "z22sb_vs_z22nosb" = c("inf_sb_z22", "inf_z22"),
  "z23sb_vs_sb" = c("inf_sb_z23", "uninf_sb_none"),
  "z22sb_vs_sb" = c("inf_sb_z22", "uninf_sb_none"),
  "z23sb_vs_uninf" = c("inf_sb_z23", "uninf_none"),
  "z22sb_vs_uninf" = c("inf_sb_z22", "uninf_none"),
  "sb_vs_uninf" = c("uninf_sb_none", "uninf_none"))
## If some cases, when the set of significant genes was chosen, an
## additional filter was added to exclude genes with expression values
## less than 'high_expression' according to the
## 'high_expression_column' in the table.
high_expression <- 128
high_expression_column <- "deseq_basemean"

combined_to_tsv <- function(combined, celltype = "all") {
  keepers <- combined[["keepers"]]
  for (k in seq_len(length(keepers))) {
    kname <- names(keepers)[k]
    numerator <- keepers[[k]][1]
    denominator <- keepers[[k]][2]
    filename <- glue("analyses/macrophage_de/tsv_tables/tmrc2_{celltype}_{kname}_n{numerator}_d{denominator}-v{ver}.xlsx")
    kdata <- combined[["data"]][[kname]]
    if (is.null(kdata[["basic_num"]])) {
      next
    }
    wanted <- c("hgnc_symbol", "deseq_logfc", "deseq_adjp",
                "deseq_basemean", "deseq_num", "deseq_den")
    wanted_data <- kdata[, wanted]
    colnames(wanted_data) <- c("hgnc_symbol", "deseq_logfc", "deseq_adjp",
                               "deseq_mean", "deseq_numerator", "deseq_denominator")
    write_xlsx(data = wanted_data, excel = filename)
  }
}

write_all_gp <- function(all_gp, suffix = NULL) {
  all_written <- list()
  for (g in seq_len(length(all_gp))) {
    name <- names(all_gp)[g]
    datum <- all_gp[[name]]
    filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler-v{ver}.xlsx")
    if (!is.null(suffix)) {
      filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler{suffix}-v{ver}.xlsx")
    }
    written <- sm(write_gprofiler_data(datum, excel = filename))
    all_written[[g]] <- written
  }
  return(all_written)
}
```

## Primary queries

There is a series of initial questions which make some sense
to me, but these do not necessarily match the set of questions which
are most pressing.  I am hoping to pull both of these sets of
queries in one.

Before extracting these groups of queries, let us invoke the
all_pairwise() function and get all of the likely contrasts along with
one or more extras that might prove useful (the 'extra' argument).

The structure of these blocks will all basically be identical:

* Perform a set of pairwise contrasts of all the conditions against
  each other.  Optionally use sva.
* Given that result, dump it in its entirety to an xlsx file in the
  analyses/ directory.
* Given those combined tables, extract from them the set deemed
  'significant' by whatever criteria we want to try. (Usually |lfc| >=
  1.0, adjusted p <= 0.05; but potentially also expression >= x and
  sometimes a set of less stringent values (|lfc| >= 0.6))
* Given one or more gene sets deemed 'significant' pass them to
  gProfiler2 and see what pops out.

### Combined U937 and Macrophages: Compare drug effects

When we have the u937 cells in the same dataset as the macrophages,
that provides an interesting opportunity to see if we can observe
drug-dependant effects which are shared across both cell types.

Note to self: given the changes to hpgltools I may need to specify the
statistical model string when I am using svaseq for some/many/all of
these comparisons.

```{r}
drug_de <- all_pairwise(all_human, filter = TRUE, model_svs = "svaseq", do_noiseq = FALSE)
drug_de

drug_table <- combine_de_tables(
  drug_de, keepers = tmrc2_drug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_drug_comparison-v{ver}.xlsx"))
drug_table
#combined_to_tsv(drug_table, celltype = "all")

drug_sig <- extract_significant_genes(
  drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_sig-v{ver}.xlsx"))
drug_sig
drug_highsig <- extract_significant_genes(
  drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_highsig-v{ver}.xlsx"))
drug_highsig
drug_lesssig <- extract_significant_genes(
  drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_drug_lesssig-v{ver}.xlsx"))
drug_lesssig
```

#### gProfiler2 results of the significant drug genes

```{r}
all_drug_gp <- all_gprofiler(drug_sig, enrich_id_column = "hgnc_symbol")
all_drug_gp
written <- write_all_gp(all_drug_gp)

all_drug_lesssig <- all_gprofiler(drug_lesssig, enrich_id_column = "hgnc_symbol")
written <- write_all_gp(all_drug_lesssig, suffix = "_lfc0.6_")
```

### Combined U937 and Macrophages: compare cell types

There are a couple of ways one might want to directly compare the two
cell types.

* Given that the variance between the two celltypes is so huge, just
compare all samples.
* One might want to compare them with the interaction effects of drug/zymodeme.

```{r}
type_de <- all_pairwise(all_human_types, filter = TRUE,
                        model_svs = "svaseq", do_noiseq = FALSE)
type_de

type_table <- combine_de_tables(
  type_de, keepers = tmrc2_type_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_comparison-v{ver}.xlsx"))
type_table
#combined_to_tsv(type_table, celltype = "all")

type_sig <- extract_significant_genes(
  type_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_sig-v{ver}.xlsx"))
type_sig
type_highsig <- extract_significant_genes(
  type_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_highsig-v{ver}.xlsx"))
type_highsig

type_lesssig <- extract_significant_genes(
  type_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_lesssig-v{ver}.xlsx"))
type_sig
```

#### Combined factors of interest: celltype+zymodeme

Given the above explicit comparison of all samples comprising the two
cell types, now let us look at the drug treatment+zymodeme status with
all samples, macrophages and U937.

```{r}
type_zymo_de <- all_pairwise(type_zymo, filter = TRUE, model_svs = "svaseq",
                             do_noiseq = FALSE,
                             extra_contrasts = type_zymo_extra)
type_zymo_de

type_zymo_table <- combine_de_tables(
  type_zymo_de, keepers = tmrc2_typezymo_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_zymo_comparison-v{ver}.xlsx"))
#combined_to_tsv(type_zymo_table, celltype = "all")

type_zymo_sig <- extract_significant_genes(
  type_zymo_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_sig-v{ver}.xlsx"))
type_zymo_sig
type_zymo_highsig <- extract_significant_genes(
  type_zymo_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_highsig-v{ver}.xlsx"))
type_zymo_lesssig <- extract_significant_genes(
  type_zymo_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_zymo_lesssig-v{ver}.xlsx"))
type_zymo_lesssig
```

#### Combined factors of interest: celltype+drug

The 'type_drug' datastructure is the same as above, but the condition
is created from the concatenation of the cell type and drug treatment.

```{r}
type_drug_de <- all_pairwise(type_drug, filter = TRUE, model_svs = "svaseq",
                             model_fstring = "~ 0 + condition")
type_drug_de
type_drug_table <- combine_de_tables(
  type_drug_de, keepers = tmrc2_typedrug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_type_drug_comparison-v{ver}.xlsx"))
type_drug_table

#combined_to_tsv(type_drug_table, celltype = "all")

type_drug_sig <- extract_significant_genes(
  type_drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_sig-v{ver}.xlsx"))
type_drug_sig

type_drug_highsig <- extract_significant_genes(
  type_drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_highsig-v{ver}.xlsx"))
type_drug_highsig

type_drug_lesssig <- extract_significant_genes(
  type_drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophage_type_drug_lesssig-v{ver}.xlsx"))
type_drug_lesssig
```

# Individual cell types

At this point, I think it is fair to say that the two cell types are
sufficiently different that they do not really belong together in a
single analysis.

## drug or strain effects, single cell type

One of the queries Najib asked which I think I misinterpreted was to
look at drug and/or strain effects.  My interpretation is somewhere
below and was not what he was looking for.  Instead, he was looking to
see all(macrophage) drug/nodrug and all(macrophage) z23/z22 and
compare them to each other.  It may be that this is still a wrong
interpretation, if so the most likely comparison is either:

*  (z23drug/z22drug) / (z23nodrug/z22nodrug), or perhaps
*  (z23drug/z23nodrug) / (z22drug/z22nodrug),

I am not sure those confuse me, and at least one of them is below

## Macrophages

In these blocks we will explicitly query only one factor at a time,
drug and strain.  The eventual goal is to look for effects of
drug treatment and/or strain treatment which are shared?

### Macrophage Drug only

Thus we will start with the pure drug query.  In this block we will
look only at the drug/nodrug effect.

```{r}
hs_macr_drug_de <- all_pairwise(hs_macr_drug_expt, filter = TRUE, model_svs = "svaseq",
                                model_fstring = "~ 0 + condition")
hs_macr_drug_de

hs_macr_drug_table <- combine_de_tables(
  hs_macr_drug_de, keepers = tmrc2_drug_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_onlydrug_table-v{ver}.xlsx"))
hs_macr_drug_table

#combined_to_tsv(hs_macr_drug_table, celltype = "macrophage")

hs_macr_drug_sig <- extract_significant_genes(
  hs_macr_drug_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_sig-v{ver}.xlsx"))
hs_macr_drug_sig

hs_macr_drug_highsig <- extract_significant_genes(
  hs_macr_drug_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_highsig-v{ver}.xlsx"))
hs_macr_drug_highsig

## Creating the following to see how it affects gProfiler.
hs_macr_drug_lesssig <- extract_significant_genes(
  hs_macr_drug_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_drug_sig_lfc0.6-v{ver}.xlsx"))
```

### Macrophage Strain only

In a similar fashion, let us look for effects which are observed when
we consider only the strain used during infection.

```{r}
hs_macr_strain_de <- all_pairwise(hs_macr_strain_expt, filter = TRUE, model_svs = "svaseq",
                                  model_fstring = "~ 0 + condition")
hs_macr_strain_de

hs_macr_strain_table <- combine_de_tables(
  hs_macr_strain_de, keepers = tmrc2_strain_keepers,
  excel = glue("analyses/macrophage_de/de_tables/macrophage_onlystrain_table-v{ver}.xlsx"))
hs_macr_strain_table
combined_to_tsv(hs_macr_strain_table, celltype = "macrophage")

hs_macr_strain_sig <- extract_significant_genes(
  hs_macr_strain_table,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_sig-v{ver}.xlsx"))
hs_macr_strain_sig

hs_macr_strain_highsig <- extract_significant_genes(
  hs_macr_strain_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_highsig-v{ver}.xlsx"))
hs_macr_strain_highsig

hs_macr_strain_lesssig <- extract_significant_genes(
  hs_macr_strain_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/macrophageonly_onlystrain_lesssig-v{ver}.xlsx"))
hs_macr_strain_lesssig
```

### Compare Drug and Strain Effects

Now let us consider the above two comparisons together.  First, I will
plot the logFC values of them against each other (drug on x-axis and
strain on the y-axis).  Then we can extract the significant genes in a
few combined categories of interest.  I assume these will focus
exclusively on the categories which include the introduction of the
drug.

```{r}
drug_strain_comp_df <- merge(hs_macr_drug_table[["data"]][["drug"]],
                             hs_macr_strain_table[["data"]][["strain"]],
                             by = "row.names")
drug_strain_comp_plot <- plot_linear_scatter(
  drug_strain_comp_df[, c("deseq_logfc.x", "deseq_logfc.y")])
## Contrasts: antimony/none, z23/z22; x-axis: drug, y-axis: strain
## top left: higher no drug, z23; top right: higher drug z23
## bottom left: higher no drug, z22; bottom right: higher drug z22
drug_strain_comp_plot[["scatter"]]
```

As I noted in the comments above, some quadrants of the scatter plot
are likely to be of greater interest to us than others (the right
side).  Because I get confused sometimes, the following block will
explicitly name the categories of likely interest, then ask which
genes are shared among them, and finally use UpSetR to extract the
various gene intersection/union categories.

```{r}
higher_drug <- hs_macr_drug_sig[["deseq"]][["downs"]][[1]]
higher_nodrug <- hs_macr_drug_sig[["deseq"]][["ups"]][[1]]
higher_z23 <- hs_macr_strain_sig[["deseq"]][["ups"]][[1]]
higher_z22 <- hs_macr_strain_sig[["deseq"]][["downs"]][[1]]
sum(rownames(higher_drug) %in% rownames(higher_z23))
sum(rownames(higher_drug) %in% rownames(higher_z22))
sum(rownames(higher_nodrug) %in% rownames(higher_z23))
sum(rownames(higher_nodrug) %in% rownames(higher_z22))

drug_z23_lst <- list("drug" = rownames(higher_drug),
                     "z23" = rownames(higher_z23))
upset_input <- UpSetR::fromList(drug_z23_lst)
higher_drug_z23 <- upset(upset_input, text.scale = 2)
higher_drug_z23

drug_z23_shared_genes <- overlap_groups(drug_z23_lst)
shared_genes_drug_z23 <- overlap_geneids(drug_z23_shared_genes, "drug:z23")
shared_genes_drug_z23 <- attr(drug_z23_shared_genes, "elements")[drug_z23_shared_genes[["drug:z23"]]]

drug_z22_lst <- list("drug" = rownames(higher_drug),
                     "z22" = rownames(higher_z22))
higher_drug_z22 <- upset(UpSetR::fromList(drug_z22_lst), text.scale = 2)
higher_drug_z22

drug_z22_shared_genes <- overlap_groups(drug_z22_lst)
shared_genes_drug_z22 <- overlap_geneids(drug_z22_shared_genes, "drug:z22")
shared_genes_drug_z22 <- attr(drug_z22_shared_genes, "elements")[drug_z22_shared_genes[["drug:z22"]]]
```

### Perform gProfiler on drug/strain effect shared genes

Now that we have some populations of genes which are shared across the
drug/strain effects, let us pass them to some GSEA analyses and see
what pops out.

```{r}
wanted <- drug_z23_shared_genes[["drug:z23"]]
shared_genes_drug_z23 <- attr(drug_z23_shared_genes, "elements")[wanted]
shared_drug_z23_gp <- simple_gprofiler(shared_genes_drug_z23)
shared_drug_z23_gp[["pvalue_plots"]][["MF"]]
shared_drug_z23_gp[["pvalue_plots"]][["BP"]]
shared_drug_z23_gp[["pvalue_plots"]][["REAC"]]

wanted <- drug_z22_shared_genes[["drug:z22"]]
shared_genes_drug_z22 <- attr(drug_z22_shared_genes, "elements")[wanted]
shared_drug_z22_gp <- simple_gprofiler(shared_genes_drug_z22)
shared_drug_z22_gp[["pvalue_plots"]][["BP"]]
```

# Our main question of interest

The data structure hs_macr contains our primary macrophages, which
are, as shown above, the data we can really sink our teeth into.

Note, we expect some errors when running the combine_de_tables()
because not all methods I use are comfortable using the ratio or
ratios contrasts we added in the 'extras' argument.  As a result, when
we combine them into the larger output tables, those peculiar
contrasts fail.  This does not stop it from writing the rest of the
results, however.

```{r}
#test = deseq_pairwise(normalize_expt(hs_macr, filter=TRUE),
#                      model_svs = "svaseq", filter = TRUE,
#                      extra_contrasts = tmrc2_human_extra)

hs_macr_de_noextra <- all_pairwise(hs_macr, model_svs = "svaseq", model_fstring = "~ 0 + condition", filter = TRUE)

hs_macr_de <- all_pairwise(hs_macr, model_svs = "svaseq", model_fstring = "~ 0 + condition",
                           filter = TRUE, extra_contrasts = tmrc2_human_extra)
hs_macr_de

hs_single_table <- combine_de_tables(
  hs_macr_de, keepers = single_tmrc2_keeper,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_z22sb_sb-v{ver}.xlsx"))
hs_single_table
hs_macr_table <- combine_de_tables(
  hs_macr_de, keepers = tmrc2_human_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_table_macr_only-v{ver}.xlsx"))
hs_macr_table
#combined_to_tsv(hs_macr_table, "macrophage")

hs_macr_sig <- extract_significant_genes(
  hs_macr_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_sig-v{ver}.xlsx"))
hs_macr_sig
hs_macr_highsig <- extract_significant_genes(
  hs_macr_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_highsig-v{ver}.xlsx"))
hs_macr_highsig
hs_macr_lesssig <- extract_significant_genes(
  hs_macr_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_sig_lfc0.6-v{ver}.xlsx"))
hs_macr_lesssig
```

## gene group upset

### 2.3 vs 2.2 up and down vs. uninfected

This is my version of the Venn diagram which includes the text:

"Differentially expressed genes in macrophages infected with
subpopulations 2.2 or 2.3.  Volcano plots contrast of: A. Venn diagram
for upregulated and downregulated genes by infection with 2.3 and 2.2
strains. B. infected cells with 2.3 strains and uninfected cells;
C. infected cells with 2.2 strains and uninfected cells; D. infected
cells with 2.3 strains and infected cells with 2.2 strains"

The following upset plot is currently Figure 2E.

```{r}
nodrug_upset <- upsetr_combined_de(hs_macr_table,
                                   desired_contrasts = c("z22nosb_vs_uninf", "z23nosb_vs_uninf"))
pp(file = "images/nodrug_upset.svg")
nodrug_upset[["plot"]]
dev.off()
nodrug_upset
```

#### A point of interest while Olga visits Umd

Najib and Olga asked about pulling the 9 gene IDs which are in the
peculiar situation of increased expression in z2.2/uninf and decreased
in z2.3/uninf.  In the previous upset plot, these are visible in the
6th bar.  I can access these via the attr() function, which I should
admit I can never remember how to use, so I am going to use the code
under the 'Compare(no)Sb z2.3/z2.2 treatment' heading to remember how
to extract these genes.

```{r}
all_groups <- nodrug_upset[["groups"]]
wanted_group <- "z23nosb_vs_uninf_down:z22nosb_vs_uninf_up"
gene_idx <- all_groups[[wanted_group]]
wanted_genes <- attr(all_groups, "elements")[gene_idx]
wanted_genes
gene_symbol_idx <- rownames(fData(hs_macr)) %in% as.character(wanted_genes)
fData(hs_macr)[gene_symbol_idx, "hgnc_symbol"]
```

* ABCB5: ATB Binding Cassette Subfamily B Member #5, wide range of
  functions in this diverse paralogous family.  Associated with skin
  diseases (melanoma and Epidermolysis Bullosa; participate in
  ATP-dependent transmembrane transport).
* RFX4: Regulatory Factor X #4: transcription factor.
* CA14: Carbonic anhydrase #14: Zync metalloenzyme catalyzes
  reversible hydration of CO2.  This gene looks pretty neat, but not
  really relevant to anything we are likely to care about.
* EGR1: Early Growth Response Protein #1: Another Tx factor
  (zinc-finger) -- important for cell survival/proliferation/cell
  death.  Presumably important for healing?
* MCF2L: MCF.2 Cell Line Derived Transforming Sequence Like?  guanine
  nucleotide exchange factor interacting with GTP-bound Rac1.
  Apparently associated with ostroarthritis; potentially relevant to
  regulation of RHOA and CDC42 signalling.
* DNASE1L3: Deoxyribonuclease I family member: not inhibited by actin,
  breaks down DNA during apoptosis.  Important during necrosis.
* FOS: Proto-Oncogene, AP-1 Transcription Factor: leucine zipper
  dimerizes with JUN family proteins, forming tx factor complex AP-1.
  Important for cell proliferation, differentiation, and
  transformation.
* IFITM10: Interferon-Induced Transmembrane Protein #10
* PKD1L3: Polycystin 1 Like #3, Transient Receptor Potential Channel
  Interacting: 11 transmembrane domain protein which might help create
  cation channels.

As some comparison points, the Venn in the current figure has:

* 387 up z2.3
* 259 up z2.2
* 83 shared up z2.3 and z2.2
* 247 down z2.3
* 3 down z2.2
* 3 shared down z2.3 and z2.2

### 2.2 and 2.3 with SbV vs 2.2 and 2.3 without SbV

This is my version of the Venn with the text:

"Differentially expressed genes in macrophages infected with
subpopulations 2.2 or 2.3, in presence of SbV. Volcano plots contrast
of: A. infected cells with 2.3 strains + SbV and infected cells with
2.3 strains; B. infected cells with 2.2 strains + SbV and infected
cells with 2.2 strains; C. infected cells with 2.3 strains + SbV and
infected cells with 2.2 strains + SbV. D. Venn diagram for upregulated
and downregulated genes by infection with 2.3+SbV and 2.2+SbV
strains."

A query from Olga (20240801): Please include in the upset in figure 3
the contrast of uninfected cells + SbV vs uninfected without SbV.

```{r}
## I keep mis-interpreting this text, it is z2.3/z2.3SbV and z2.2/z2.2SbV
drugnodrug_upset <- upsetr_combined_de(hs_macr_table,
                                       desired_contrasts = c("z23sb_vs_z23nosb", "z22sb_vs_z22nosb"))
pp(file = "images/drugnodrug_upset.pdf")
drugnodrug_upset[["plot"]]
dev.off()
drugnodrug_upset

drugnodrug_uninf_contrasts <- c("z23sb_vs_z23nosb", "z22sb_vs_z22nosb", "sb_vs_uninf")
drugnodrug_upset_with_uninf <- upsetr_combined_de(hs_macr_table,
                                       desired_contrasts = drugnodrug_uninf_contrasts)
pp(file = "figures/drugnodrug_with_uninf_upset.svg")
drugnodrug_upset_with_uninf[["plot"]]
dev.off()
drugnodrug_upset_with_uninf
```

For some comparison points, the venn image has:

* 222 up z2.3 SbV
* 134 up z2.2 SbV
* 182 down z2.3 SbV
* 396 down z2.2 SbV
* 605 shared down z2.2 and z2.3 SbV
* 34 shared down z2.2 SbV and up z2.3 SbV
* 363 shared up z2.2 SbV and z2.3 SbV

### Compare z2.2SbV vs SbV and z2.3SbV and SbV

```{r}
drug_upset <- upsetr_combined_de(hs_macr_table,
                                 desired_contrasts = c("z22sb_vs_sb", "z23sb_vs_sb"))
pp(file = "images/drug_upset.pdf")
drug_upset[["plot"]]
dev.off()
drug_upset
```

## Significance barplot of interest

Olga kindly sent a set of particularly interesting contrasts and
colors for a significance barplot, they include the following:

* z2.3 vs. uninfected.
* z2.2 vs. uninfected.
* z2.3 vs z2.2
* z2.3Sbv vs z2.3
* z2.2Sbv vs z2.2
* z2.3Sbv vs z2.2Sbv
* Sbv vs uninfected.

The existing set of 'keepers' exvised to these is taken from the
extant set of 'tmrc2_human_keepers' and is as follows:


```{r}
barplot_keepers <- list(
  ## z2.3 vs uninfected
  "z23nosb_vs_uninf" = c("infz23", "uninfnone"),
  ## z2.2 vs uninfected
  "z22nosb_vs_uninf" = c("infz22", "uninfnone"),
  ## z2.3 vs z2.2
  "z23nosb_vs_z22nosb" = c("infz23", "infz22"),
  ## z2.3Sbv vs z2.3
  "z23sb_vs_z23nosb" = c("infsbz23", "infz23"),
  ## z2.2Sbv vs z2.2
  "z22sb_vs_z22nosb" = c("infsbz22", "infz22"),
  ## z2.3Sbv vs z2.2Sbv
  "z23sb_vs_z22sb" = c("infsbz23", "infsbz22"),
  ## Sbv vs uninfected.
  "sb_vs_uninf" = c("uninfsbnone", "uninfnone"))
barplot_combined <- combine_de_tables(
  hs_macr_de, keepers = barplot_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_macr_drug_zymo_7contrasts-v{ver}.xlsx"))
```

Now let us use the colors suggested by Olga to make a barplot of
these...

```{r}
color_list <-  c( "#de8bf9", "#ad07e3","#410257", "#ffa0a0", "#f94040", "#a00000")
barplot_sig <- extract_significant_genes(
  barplot_combined, color_list = color_list, according_to = "deseq",
  excel = glue("analyses/macrophage_de/sig_tables/hs_macr_drug_zymo_7contrasts_sig-v{ver}.xlsx"))
barplot_sig
```

# PROPER

In our last meeting there were some questions about the statistical
power of different future experimental designs.  One thing I can do is
to use PROPER to estimate the power of an extant dataset and infer
from that the likely power of other designs.

In order to use proper, one must feed it one or more DE tables.

```{r}
power_estimate <- simple_proper(hs_single_table)

power_estimate[[1]][["power_plot"]]
power_estimate[[1]][["powertd_plot"]]
power_estimate[[1]][["powerfd_plot"]]
```

# Our main questions in U937

Let us do the same comparisons in the U937 samples, though I will not
do the extra contrasts, primarily because I think the dataset is less
likely to support them.

```{r}
u937_de <- all_pairwise(u937_expt, model_svs = "svaseq",
                        filter = TRUE, do_noiseq = FALSE)
u937_de
u937_table <- combine_de_tables(
  u937_de, keepers = u937_keepers,
  excel = glue("analyses/macrophage_de/de_tables/u937_drug_zymo_table-v{ver}.xlsx"))
u937_table
combined_to_tsv(u937_table, celltype = "u937")

u937_sig <- extract_significant_genes(
  u937_table,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_sig-v{ver}.xlsx"))
u937_sig
u937_highsig <- extract_significant_genes(
  u937_table, min_mean_exprs = high_expression, exprs_column = high_expression_column,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_highsig-v{ver}.xlsx"))
u937_highsig
u937_lesssig <- extract_significant_genes(
  u937_table, lfc = 0.6,
  excel = glue("analyses/macrophage_de/sig_tables/u937_drug_zymo_lesssig-v{ver}.xlsx"))
u937_lesssig
```

# Compare (no)Sb z2.3/z2.2 treatments among macrophages

In the following block, I will jump back to the macrophage samples and
look for genes which are shared/unique when comparing z2.3/z2.2
for the drug treated samples and the untreated samples.

```{r}
upset_plots_hs_macr <- upsetr_sig(
  hs_macr_sig, both = TRUE,
  contrasts = c("z23sb_vs_z22sb", "z23nosb_vs_z22nosb"))
upset_plots_hs_macr[["both"]]
groups <- upset_plots_hs_macr[["both_groups"]]
shared_genes <- attr(groups, "elements")[groups[[2]]] %>%
  gsub(pattern = "^gene:", replacement = "")
length(shared_genes)

shared_gp <- simple_gprofiler(shared_genes)
shared_gp[["pvalue_plots"]][["MF"]]
shared_gp[["pvalue_plots"]][["BP"]]
shared_gp[["pvalue_plots"]][["REAC"]]

drug_genes <- attr(groups, "elements")[groups[["z23sb_vs_z22sb"]]] %>%
  gsub(pattern = "^gene:", replacement = "")
drugonly_gp <- simple_gprofiler(drug_genes)
drugonly_gp[["pvalue_plots"]][["BP"]]
```

I want to try something, directly include the u937 data in this.
Thus, in the following block I will repeat but compare all samples and
the U937 using the same logic.

```{r}
both_sig <- hs_macr_sig
names(both_sig[["deseq"]][["ups"]]) <- paste0("macr_", names(both_sig[["deseq"]][["ups"]]))
names(both_sig[["deseq"]][["downs"]]) <- paste0("macr_", names(both_sig[["deseq"]][["downs"]]))
u937_deseq <- u937_sig[["deseq"]]
names(u937_deseq[["ups"]]) <- paste0("u937_", names(u937_deseq[["ups"]]))
names(u937_deseq[["downs"]]) <- paste0("u937_", names(u937_deseq[["downs"]]))
both_sig[["deseq"]][["ups"]] <- c(both_sig[["deseq"]][["ups"]], u937_deseq[["ups"]])
both_sig[["deseq"]][["downs"]] <- c(both_sig[["deseq"]][["ups"]], u937_deseq[["downs"]])
summary(both_sig[["deseq"]][["ups"]])

upset_plots_both <- upsetr_sig(
  both_sig, both = TRUE,
  contrasts = c("macr_z23sb_vs_z22sb", "macr_z23nosb_vs_z22nosb",
                "u937_z23sb_vs_z22sb", "u937_z23nosb_vs_z22nosb"))
upset_plots_both[["both"]]
```

## Compare DE results from macrophages and U937 samples

Looking a bit more closely at these, I think the u937 data is too
sparse to effectively compare.

```{r}
macr_u937_comparison <- compare_de_results(hs_macr_table, u937_table)
macr_u937_comparison[["lfc_heat"]]

macr_u937_venns <- compare_significant_contrasts(hs_macr_sig, second_sig_tables = u937_sig,
                                                 contrasts = "z23sb_vs_z23nosb")
macr_u937_venns[["up_plot"]]
macr_u937_venns[["down_plot"]]

macr_u937_venns_v2 <- compare_significant_contrasts(
  hs_macr_sig, second_sig_tables = u937_sig, contrasts = "z22sb_vs_z22nosb")
macr_u937_venns_v2[["up_plot"]]
macr_u937_venns_v2[["down_plot"]]

macr_u937_venns_v3 <- compare_significant_contrasts(
  hs_macr_sig, second_sig_tables = u937_sig, contrasts = "sb_vs_uninf")
macr_u937_venns_v3[["up_plot"]]
macr_u937_venns_v3[["down_plot"]]
```

## Compare macrophage/u937 with respect to z2.3/z2.2

```{r}
comparison_df <- merge(hs_macr_table[["data"]][["z23sb_vs_z22sb"]],
                       u937_table[["data"]][["z23sb_vs_z22sb"]],
                       by = "row.names")
macru937_z23z22_plot <- plot_linear_scatter(comparison_df[, c("deseq_logfc.x", "deseq_logfc.y")])
macru937_z23z22_plot[["scatter"]]

comparison_df <- merge(hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]],
                       u937_table[["data"]][["z23nosb_vs_z22nosb"]],
                       by = "row.names")
macru937_z23z22_plot <- plot_linear_scatter(comparison_df[, c("deseq_logfc.x", "deseq_logfc.y")])
macru937_z23z22_plot[["scatter"]]
```

# Add donor to the contrasts, no sva

In the following block, I will change the sample condition to include
the donor.

```{r}
no_power_fact <- paste0(pData(hs_macr)[["donor"]], "_",
                        pData(hs_macr)[["condition"]])
table(pData(hs_macr)[["donor"]])
table(no_power_fact)
hs_nopower <- set_expt_conditions(hs_macr, fact = no_power_fact)
hs_nopower <- subset_expt(hs_nopower, subset = "macrophagezymodeme!='none'")
hs_nopower_nosva_de <- all_pairwise(hs_nopower, model_svs = FALSE, filter = TRUE)
nopower_keepers <- list(
  "d01_zymo" = c("d01infz23", "d01infz22"),
  "d01_sbzymo" = c("d01infsbz23", "d01infsbz22"),
  "d02_zymo" = c("d02infz23", "d02infz22"),
  "d02_sbzymo" = c("d02infsbz23", "d02infsbz22"),
  "d09_zymo" = c("d09infz23", "d09infz22"),
  "d09_sbzymo" = c("d09infsbz23", "d09infsbz22"),
  "d81_zymo" = c("d81infz23", "d81infz22"),
  "d81_sbzymo" = c("d81infsbz23", "d81infsbz22"))
hs_nopower_nosva_table <- combine_de_tables(
  hs_nopower_nosva_de, keepers = nopower_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_nopower_table-v{ver}.xlsx"))
## extra_contrasts = extra)
hs_nopower_nosva_sig <- extract_significant_genes(
  hs_nopower_nosva_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_nopower_nosva_sig-v{ver}.xlsx"))

d01d02_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d01_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d02_zymo"]],
                                by = "row.names")
d0102_zymo_nosva_plot <- plot_linear_scatter(d01d02_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0102_zymo_nosva_plot[["scatter"]]
d0102_zymo_nosva_plot[["correlation"]]
d0102_zymo_nosva_plot[["lm_rsq"]]

d09d81_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d09_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d81_zymo"]],
                                by = "row.names")
d0981_zymo_nosva_plot <- plot_linear_scatter(d09d81_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0981_zymo_nosva_plot[["scatter"]]
d0981_zymo_nosva_plot[["correlation"]]
d0981_zymo_nosva_plot[["lm_rsq"]]

d01d81_zymo_nosva_comp <- merge(hs_nopower_nosva_table[["data"]][["d01_zymo"]],
                                hs_nopower_nosva_table[["data"]][["d81_zymo"]],
                                by = "row.names")
d0181_zymo_nosva_plot <- plot_linear_scatter(d01d81_zymo_nosva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0181_zymo_nosva_plot[["scatter"]]
d0181_zymo_nosva_plot[["correlation"]]
d0181_zymo_nosva_plot[["lm_rsq"]]

upset_plots_nosva <- upsetr_sig(hs_nopower_nosva_sig, both = TRUE,
                                contrasts = c("d01_zymo", "d02_zymo", "d09_zymo", "d81_zymo"))
upset_plots_nosva[["up"]]
upset_plots_nosva[["down"]]
upset_plots_nosva[["both"]]
## The 7th element in the both groups list is the set shared among all donors.
## I don't feel like writing out x:y:z:a
groups <- upset_plots_nosva[["both_groups"]]
shared_genes <- attr(groups, "elements")[groups[[7]]] %>%
  gsub(pattern = "^gene:", replacement = "")
shared_gp <- simple_gprofiler(shared_genes)
shared_gp[["pvalue_plots"]][["MF"]]
shared_gp[["pvalue_plots"]][["BP"]]
shared_gp[["pvalue_plots"]][["REAC"]]
shared_gp[["pvalue_plots"]][["WP"]]
```

# Add donor to the contrasts, sva

Same deal as the last block, but this time add SVA into the mix!

```{r}
hs_nopower_sva_de <- all_pairwise(hs_nopower, model_svs = "svaseq", filter = TRUE)
nopower_keepers <- list(
  "d01_zymo" = c("d01infz23", "d01infz22"),
  "d01_sbzymo" = c("d01infsbz23", "d01infsbz22"),
  "d02_zymo" = c("d02infz23", "d02infz22"),
  "d02_sbzymo" = c("d02infsbz23", "d02infsbz22"),
  "d09_zymo" = c("d09infz23", "d09infz22"),
  "d09_sbzymo" = c("d09infsbz23", "d09infsbz22"),
  "d81_zymo" = c("d81infz23", "d81infz22"),
  "d81_sbzymo" = c("d81infsbz23", "d81infsbz22"))
hs_nopower_sva_table <- combine_de_tables(
  hs_nopower_sva_de, keepers = nopower_keepers,
  excel = glue("analyses/macrophage_de/de_tables/hs_nopower_table-v{ver}.xlsx"))
## extra_contrasts = extra)
hs_nopower_sva_sig <- extract_significant_genes(
  hs_nopower_sva_table,
  excel = glue("analyses/macrophage_de/sig_tables/hs_nopower_sva_sig-v{ver}.xlsx"))

d01d02_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d01_zymo"]],
                              hs_nopower_sva_table[["data"]][["d02_zymo"]],
                              by = "row.names")
d0102_zymo_sva_plot <- plot_linear_scatter(d01d02_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0102_zymo_sva_plot[["scatter"]]
d0102_zymo_sva_plot[["correlation"]]
d0102_zymo_sva_plot[["lm_rsq"]]

d09d81_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d09_zymo"]],
                              hs_nopower_sva_table[["data"]][["d81_zymo"]],
                              by = "row.names")
d0981_zymo_sva_plot <- plot_linear_scatter(d09d81_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0981_zymo_sva_plot[["scatter"]]
d0981_zymo_sva_plot[["correlation"]]
d0981_zymo_sva_plot[["lm_rsq"]]

d01d81_zymo_sva_comp <- merge(hs_nopower_sva_table[["data"]][["d01_zymo"]],
                              hs_nopower_sva_table[["data"]][["d81_zymo"]],
                              by = "row.names")
d0181_zymo_sva_plot <- plot_linear_scatter(d01d81_zymo_sva_comp[, c("deseq_logfc.x", "deseq_logfc.y")])
d0181_zymo_sva_plot[["scatter"]]
d0181_zymo_sva_plot[["correlation"]]
d0181_zymo_sva_plot[["lm_rsq"]]

upset_plots_sva <- upsetr_sig(hs_nopower_sva_sig, both = TRUE,
                              contrasts = c("d01_zymo", "d02_zymo", "d09_zymo", "d81_zymo"))
upset_plots_sva[["up"]]
upset_plots_sva[["down"]]
upset_plots_sva[["both"]]
## The 7th element in the both groups list is the set shared among all donors.
## I don't feel like writing out x:y:z:a
groups <- upset_plots_sva[["both_groups"]]
shared_genes <- attr(groups, "elements")[groups[[7]]] %>%
  gsub(pattern = "^gene:", replacement = "")
shared_gp <- simple_gprofiler(shared_genes)
shared_gp[["pvalue_plots"]][["MF"]]
shared_gp[["pvalue_plots"]][["BP"]]
shared_gp[["pvalue_plots"]][["REAC"]]
shared_gp[["pvalue_plots"]][["WP"]]
```

# Donor comparison

Now compare the donors to each other directly.

```{r}
hs_donors <- set_expt_conditions(hs_macr, fact = "donor")
donor_de <- all_pairwise(hs_donors, model_svs = "svaseq", filter = TRUE)
donor_de
donor_table <- combine_de_tables(
  donor_de,
  excel = glue("analyses/macrophage_de/de_tables/donor_tables-v{ver}.xlsx"))
donor_table
donor_sig <- extract_significant_genes(
  donor_table,
  excel = glue("analyses/macrophage_de/sig_tables/donor_sig-v{ver}.xlsx"))
donor_sig
```

# Primary query contrasts

The final contrast in this list is interesting because it depends on
the extra contrasts applied to the all_pairwise() above.  In my way of
thinking, the primary comparisons to consider are either cross-drug or
cross-strain, but not both.  However I think in at least a few
instances Olga is interested in strain+drug / uninfected+nodrug.

## Write contrast results

Now let us write out the xlsx file containing the above contrasts.
The file with the suffix _table-version will therefore contain all
genes and the file with the suffix _sig-version will contain only
those deemed significant via our default criteria of DESeq2 |logFC| >= 1.0
and adjusted p-value <= 0.05.

## Over representation searches

I decided to make one initially small, but I think quickly big change
to the organization of this document:  I am moving the GSEA searches
up to immediately after the DE.  I will then move the plots of the
gprofiler results to immediately after the various volcano plots so
that it is easier to interpret them.

I am reasonably certain this is the place to check that z23no drug /
uninfected has the expected set of genes and that there is or is not a
reactome result.

Reproducibility note: Given that this is entirely dependent on an
online service, I must assume that the results will change over time;
in addition their web servers undergo maintenance regularly, which may
result in systematic failure of these analyses.  I like gProfiler
quite a lot for this type of stuff, but this is an important caveat.

Conversely, the clusterProfiler results later depend on a consistent
orgdb annotation set (or reactome or whatever); those versions are
fixed by the container installation.

```{r}
all_gp <- all_gprofiler(hs_macr_sig, enrich_id_column = "hgncsymbol")
for (g in seq_len(length(all_gp))) {
  name <- names(all_gp)[g]
  datum <- all_gp[[name]]
  filename <- glue("analyses/macrophage_de/gprofiler/{name}_gprofiler-v{ver}.xlsx")
  written <- sm(write_gprofiler_data(datum, excel = filename))
}
```

## Explicit GSEA search vis clusterProfiler

```{r}
all_cp <- all_cprofiler(hs_macr_sig, hs_macr_table)
```

## Specific desires in Reactome results

In previous analyses (I think by Dr. Colmenares), a specific
Tryptophan biosynthesis pathway was observed.  Partciularly in the
2.3/uninfected comparison.  I think my gprofiler analysis is too
stringent and therefore not observing this.  Olga asked if I could
look at that and see if there are trivial settings I can change to
highlight this pathway.  The two most likely things I can change are
the stringencies of the DE analysis and/or gProfiler.

```{r}
test_z23_uninf_up <- hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]]
nrow(test_z23_uninf_up)
test_z23_uninf_down <- hs_macr_sig[["deseq"]][["downs"]][["z23nosb_vs_uninf"]]
nrow(test_z23_uninf_down)

test_gp_up <- simple_gprofiler(test_z23_uninf_up, enrich_id_column = "hgncsymbol",
                               threshold = 1.0)
test_gp_up
written_up <- write_gprofiler_data(test_gp_up, excel = "excel/z23_uninf_gp_up_all.xlsx")

test_gp_down <- simple_gprofiler(test_z23_uninf_down, enrich_id_column = "hgncsymbol",
                                 threshold = 1.0)
test_gp_down
written_down <- write_gprofiler_data(test_gp_down, excel = "excel/z23_uninf_gp_down_all.xlsx")
```

## Plot contrasts of interest

One suggestion I received recently was to set the axes for these
volcano plots to be static rather than let ggplot choose its own.  I
am assuming this is only relevant for pairs of contrasts, but that
might not be true.

## Individual zymodemes vs. uninfected

The following blocks will be a lot of repetition.  In each case I am
yanking out the volcano plot for a specific contrast and showing the
original followed by a version with different colors/labelling.

### Infected with z2.3 no Antimonial vs. Uninfected

```{r}
plot_colors <- get_expt_colors(hs_macr_table[["input"]][["input"]])

## The original plot from my xlsx file
hs_macr_table[["plots"]][["z23nosb_vs_uninf"]][["deseq_vol_plots"]]

z23nosb_vs_uninf_volcano <- plot_volcano_condition_de(
  input = hs_macr_table[["data"]][["z23nosb_vs_uninf"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["uninfnone"]], color_high = plot_colors[["infz23"]])

labeled <- z23nosb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(limits = c(-6, 21), breaks = c(-6, -4, -2, 0, 2, 4, 6, 8, 10, 20)) +
  ggbreak::scale_x_break(c(10, 19), scales = 0.2, space = 0.02)
pp(file = "figures/fig2a_labeled_with_break.svg")
labeled
dev.off()
labeled

plotly::ggplotly(z23nosb_vs_uninf_volcano[["plot"]])
```

The following provides some of the over-representation plots from gProfiler2.

```{r}
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["KEGG"]]
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
##all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_uninf_up"]][["interactive_plots"]][["WP"]]

message("Olga received a query about the following result, I think it is null.")
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["REAC"]]
message("Is the previous plot null?")
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_uninf_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

We have some other categorical enrichment plots available via
enrichplot, let us try a few out for contrasts of interest and see if
any of them prove helpful.

First, as a reminder, here are the contrasts which are available to
examine, in each case there is an _up and _down enrichment object in
the data.  Thus in the following list I am going to arbitrarily print
out some invocations which extract putatively interesting bits of data.

* z23nosb_vs_uninf: all_gp[["z23nosb_vs_uninf_up"]][["BP_enrich"]]
* z22nosb_vs_uninf.
* z23nosb_vs_z22nosb.
* z23sb_vs_z22sb.
* z23sb_vs_z23nosb.
* z22sb_vs_z22nosb.
* z23sb_vs_sb.
* z22sb_vs_sb.
* z23sb_vs_uninf.
* z22sb_vs_uninf.
* sb_vs_uninf.
* extra_z2322.
* extra_drugnodrug.

```{r}
z23nosb_uninf_up_go <- all_gp[["z23nosb_vs_uninf_up"]][["BP_enrich"]]
z23nosb_uninf_up_go_pair <- pairwise_termsim(z23nosb_uninf_up_go)
dotplot(z23nosb_uninf_up_go)
emapplot(z23nosb_uninf_up_go_pair)
##ssplot(z23nosb_uninf_up_go_pair)
treeplot(z23nosb_uninf_up_go_pair)
upsetplot(z23nosb_uninf_up_go)
cnetplot(z23nosb_uninf_up_go)
```

### Repeat, but using a less strict set of 'significant genes'

I am not entirely certain if the Reactome results Olga showed me
included both up and down genes?  I am going to assume for the moment
that it was just up/down, but if that proves intractable I will go
back to the manuscript and read more carefully (e.g. I just remembered
where the picture came from!)

#### Add a little topgo

In the process of exploring the various parameters used with
gProfiler2, I found myself thinking that it would be nice to have some
topgo results to compare against.  The following block is the result
of that thought.

```{r}
test_genes_up <- hs_macr_lesssig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]]
test_query_up <- simple_gprofiler(test_genes_up, threshold = 0.1)
test_query_up[["pvalue_plots"]][["REAC"]]
pdf(file = "images/test_query_biological_process_z23_vs_uninf_up.pdf", height = 12, width = 9)
test_query_up[["pvalue_plots"]][["BP"]]
dev.off()
enrichplot::dotplot(test_query_up[["BP_enrich"]])
test_genes_down <- hs_macr_lesssig[["deseq"]][["downs"]][["z23nosb_vs_uninf"]]
test_query_down <- simple_gprofiler(test_genes_down)
test_query_down[["pvalue_plots"]][["REAC"]]

## I keep getting all sorts of annoying biomart errors.
hs_go <- try(load_biomart_go(archive = FALSE, overwrite = TRUE))
if ("try-error" %in% class(hs_go)) {
  hs_go <- load_biomart_go(archive = TRUE, month = "04", year = "2020", overwrite = TRUE)
}
test_topgo_up <- simple_topgo(test_genes_up, go_db = hs_go[["go"]], parallel = FALSE)
written_topgo <- write_topgo_data(
  test_topgo_up,
  excel = glue("analyses/macrophage_de/ontology_topgo/topgo_z23_uninf_less_strict.xlsx"))
```

### Infected with z2.2 no Antimonial vs. Uninfected

Here is where things will get most repetitive.  In each instance I am
creating a couple of volcano plots followed by printing some of the
gProfiler2 results (when I get the itch).

The following should be a slightly improved version of our extant
figure 2B.

```{r}
## The original plot
hs_macr_table[["plots"]][["z22nosb_vs_uninf"]][["deseq_vol_plots"]]

z22nosb_vs_uninf_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22nosb_vs_uninf"]], "z22nosb_vs_uninf",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["uninfnone"]], color_high = plot_colors[["infz22"]])

labeled <- z22nosb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(limits = c(-2, 21), breaks = c(-2, 0, 2, 4, 6, 8, 10, 21, 22)) +
  ggbreak::scale_x_break(c(11, 20), scales = 0.2, space = 0.02)
pp(file = "figures/fig2b_labeled_with_break.svg")
labeled
dev.off()
labeled

plotly::ggplotly(z22nosb_vs_uninf_volcano[["plot"]])
```

Add some pvalue barplots from gProfiler for this contrast.

```{r}
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.2 without drug vs. uninfected without drug, up.
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.2 without drug vs. uninfected without drug, up.

all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.2 without drug vs. uninfected without drug, down.
all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.2 without drug vs. uninfected without drug, down.
all_gp[["z22nosb_vs_uninf_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

### Infected with z2.3 treated vs. Uninfected treated

I do not think this plot is used at this time.

```{r}
## The original plot
hs_macr_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]]

z23sb_vs_uninfsb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_sb"]], "z23sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["infsbz23"]], color_high = plot_colors[["uninfsbnone"]])
z23sb_vs_uninfsb_volcano[["plot"]]

plotly::ggplotly(z23sb_vs_uninfsb_volcano[["plot"]])
```

### Infected with z2.3 untreated vs. z2.2 untreated

This is figure 2C at this time.

```{r}
## The original plot
hs_macr_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]]

z23nosb_vs_z22nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]], "z23nosb_vs_z22nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_low = plot_colors[["infz23"]], color_high = plot_colors[["infz22"]])

labeled <- z23nosb_vs_z22nosb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-10, -8, -6, -4, -2, 0, 2, 4, 6))

pp(file = "figures/fig2c_labeled.svg")
labeled
dev.off()
labeled
```

### Infected with z2.3 treated vs. z2.2 treated

This is currently figure 3C.

FIXME: The axis label isn't quite right for the ggbreak.

```{r}
## The original plot
hs_macr_table[["plots"]][["z23sb_vs_z22sb"]][["deseq_vol_plots"]]

z23sb_vs_z22sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_z22sb"]], "z23sb_vs_z22sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz23"]], color_low = plot_colors[["infsbz22"]])

labeled <- z23sb_vs_z22sb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-23, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-5, -22.5), scales = 10, space = 0.02)
pp(file = "figures/fig3c_labeled_breaks.svg")
labeled
dev.off()
labeled
```

### Infected with z2.3 SB treated vs. z2.3 untreated

I think this is currently figure 3A.

FIXME: The axis label for the ggbreak isn't quite right.

```{r}
## The original plot
hs_macr_table[["plots"]][["z23sb_vs_z23nosb"]][["deseq_vol_plots"]]

z23sb_vs_z23nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_z23nosb"]], "z23sb_vs_z23nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz23"]], color_low = plot_colors[["infz23"]])

labeled <- z23sb_vs_z23nosb_volcano[["plot"]] +
  scale_x_continuous(limits = c(-19, 6),
                     breaks = c(-20, -18, -16, -14, -12, -10, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-17, -8), scales = 17, space = 0.02)
pp(file = "figures/fig3a_labeled_with_break.svg")
labeled
dev.off()
labeled
```

### Infected with z2.3 SB treated vs. z2.3 untreated

```{r}
## The original plot
hs_macr_table[["plots"]][["z22sb_vs_z22nosb"]][["deseq_vol_plots"]]

z22sb_vs_z22nosb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22sb_vs_z22nosb"]], "z22sb_vs_z22nosb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["infsbz22"]], color_low = plot_colors[["infz22"]])

labeled <- z22sb_vs_z22nosb_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-6, -4, -2, 0, 2, 4, 6))

pp(file = "figures/fig3b_labeled.svg")
labeled
dev.off()
labeled
```

### Infected with z2.3 SB treated vs. uninfected treated

```{r}
x_limits <- c(-6, 6)
## The original plot
hs_macr_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]]

z23sb_vs_sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z23sb_vs_sb"]], "z23sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol", invert = TRUE,
  color_low = plot_colors[["infsbz23"]], color_high = plot_colors[["uninfsbnone"]])
z23sb_vs_sb_volcano[["plot"]]
```

### Infected with z2.2 SB treated vs. uninfected treated

```{r}
## The original plot
hs_macr_table[["plots"]][["z22sb_vs_sb"]][["deseq_vol_plots"]]

z22sb_vs_sb_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["z22sb_vs_sb"]], "z22sb_vs_sb",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol", invert = TRUE,
  color_low = plot_colors[["infsbz22"]], color_high = plot_colors[["uninfsbnone"]])
z22sb_vs_sb_volcano[["plot"]]
```

### Uninfected+SbV vs. Uninfected-SbV

This is currently figure 3D.

FIXME: This needs the BOLA2B ggbreak.

```{r}
## The original plot
hs_macr_table[["plots"]][["sb_vs_uninf"]][["deseq_vol_plots"]]

sb_vs_uninf_volcano <- plot_volcano_condition_de(
  hs_macr_table[["data"]][["sb_vs_uninf"]], "sb_vs_uninf",
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  label = 10, label_column = "hgncsymbol",
  color_high = plot_colors[["uninfsbnone"]], color_low = plot_colors[["uninfnone"]])

labeled <- sb_vs_uninf_volcano[["plot"]] +
  scale_x_continuous(breaks = c(-23, -6, -4, -2, 0, 2, 4, 6)) +
  ggbreak::scale_x_break(c(-5, -22.5), scales = 10, space = 0.02)
pp(file = "figures/fig3d_labeled_breaks.svg")
labeled
dev.off()
labeled
```

## Double-check that gene counts match my perceptions

Check that my perception of the number of significant up/down genes
matches what the table/venn says.  In the following block I am
performing some venn/upset analyses to see if the numbers of genes
match what we have in the current version of the manuscript (plus or
minus a gene) and thus if my interpretation of the figure/legend text
matches what I think it means.

```{r}
shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_uninf"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_uninf"]])))
pp(file = "images/z23_vs_uninf_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)
## I see 910 z23sb/uninf and 670 no z23nosb/uninf genes in the venn diagram.
length(shared@IntersectionSets[["10"]]) + length(shared@IntersectionSets[["11"]])
dim(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_uninf"]])

shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_uninf"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22nosb_vs_uninf"]])))
pp(file = "images/z22_vs_uninf_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)

length(shared@IntersectionSets[["10"]]) + length(shared@IntersectionSets[["11"]])
dim(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_uninf"]])
```

*Note to self*: There is an error in my volcano plot code which takes
effect when the numerator and denominator of the all_pairwise
contrasts are different than those in combine_de_tables.  It is
putting the ups/downs on the correct sides of the plot, but calling
the down genes 'up' and vice-versa.  The reason for this is that I did
a check for this happening, but used the wrong argument to handle it.

A likely bit of text for these volcano plots:

The set of genes differentially expressed between the zymodeme 2.3
and uninfected samples without druge treatment was quantified with
DESeq2 and included surrogate estimates from SVA.  Given the criteria
of significance of a abs(logFC) >= 1.0 and false discovery rate
adjusted p-value <= 0.05, 670 genes were observed as significantly
increased between the infected and uninfected samples and 386 were
observed as decreased. The most increased genes from the uninfected
samples include some which are potentially indicative of a strong
innate immune response and the inflammatory response.

In contrast, when the set of genes differentially expressed between
the zymodeme 2.2 and uninfected samples was visualized, only 7 genes
were observed as decreased and 435 increased.  The inflammatory
response was significantly less apparent in this set, but instead
included genes related to transporter activity and oxidoreductases.

## Direct zymodeme comparisons

An orthogonal comparison to that performed above is to directly
compare the zymodeme 2.3 and 2.2 samples with and without antimonial
treatment.

### Z2.3 / z2.2 without drug

```{r}
z23nosb_vs_z22nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
plotly::ggplotly(z23nosb_vs_z22nosb_volcano[["plot"]])

z23sb_vs_z22sb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23sb_vs_z22sb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
plotly::ggplotly(z23sb_vs_z22sb_volcano[["plot"]])
```

```{r}
z23nosb_vs_z22nosb_volcano[["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)

pp(file = "images/z23nosb_vs_z22nosb_reactome_up.svg",
   image = all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["KEGG"]]
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23nosb_vs_z22nosb_up"]][["interactive_plots"]][["WP"]]

pp(file = "images/z23nosb_vs_z22nosb_reactome_down.svg",
   image = all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23nosb_vs_z22nosb_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

### z2.3 / z2.2 with drug

```{r}
z23sb_vs_z22sb_volcano[["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)

pp(
  file = "images/z23sb_vs_z22sb_reactome_up.png",
  image = all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["REAC"]],
  height = 12, width = 9)
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["KEGG"]]
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z22sb_up"]][["interactive_plots"]][["WP"]]

all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

### Venn to see shared/unique genes

Once again I wish to pull out the significant genes and see how my
numbers match against the text.

```{r}
shared <- Vennerable::Venn(list(
  "drug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_z22sb"]]),
  "nodrug" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23nosb_vs_z22nosb"]])))
pp(file = "images/drug_nodrug_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)

shared <- Vennerable::Venn(
  list("drug" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23sb_vs_z22sb"]]),
       "nodrug" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23nosb_vs_z22nosb"]])))
pp(file = "images/drug_nodrug_venn_down.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)
```

A slightly different way of looking at the differences between the two
zymodeme infections is to directly compare the infected samples with
and without drug.  Thus, when a volcano plot showing the comparison of
the zymodeme 2.3 vs. 2.2 samples was plotted, 484 genes were observed
as increased and 422 decreased; these groups include many of the same
inflammatory (up) and membrane (down) genes.

Similar patterns were observed when the antimonial was included.
Thus, when a Venn diagram of the two sets of increased genes was
plotted, a significant number of the genes was observed as increased
(313) and decreased (244) in both the untreated and antimonial treated
samples.

## Drug effects on each zymodeme infection

Another likely question is to directly compare the treated vs
untreated samples for each zymodeme infection in order to visualize
the effects of antimonial.

### z2.3 with and without drug

```{r}
z23sb_vs_z23nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z23sb_vs_z23nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
plotly::ggplotly(z23sb_vs_z23nosb_volcano[["plot"]])
z22sb_vs_z22nosb_volcano <- plot_volcano_de(
  table = hs_macr_table[["data"]][["z22sb_vs_z22nosb"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
plotly::ggplotly(z22sb_vs_z22nosb_volcano[["plot"]])
```

```{r}
z23sb_vs_z23nosb_volcano[["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)

pp(file = "images/z23sb_vs_z23nosb_reactome_up.png",
   image = all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["KEGG"]]
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z23sb_vs_z23nosb_up"]][["interactive_plots"]][["WP"]]

all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z23sb_vs_z23nosb_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

### z2.2 with and without drug

```{r}
z22sb_vs_z22nosb_volcano[["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)

pp(file = "images/z22sb_vs_z22nosb_reactome_up.png",
   image = all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]],
   height = 12, width = 9)
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["KEGG"]]
## KEGG, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["WP"]]
## WikiPathways, zymodeme2.3 without drug vs. uninfected without drug, up.
all_gp[["z22sb_vs_z22nosb_up"]][["interactive_plots"]][["WP"]]

all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["REAC"]]
## Reactome, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["MF"]]
## MF, zymodeme2.3 without drug vs. uninfected without drug, down.
all_gp[["z22sb_vs_z22nosb_down"]][["pvalue_plots"]][["TF"]]
## TF, zymodeme2.3 without drug vs. uninfected without drug, down.
```

### Shared and unique genes after/before drug

```{r}
shared <- Vennerable::Venn(list(
  "z23" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z23sb_vs_z23nosb"]]),
  "z22" = rownames(hs_macr_sig[["deseq"]][["ups"]][["z22sb_vs_z22nosb"]])))
pp(file = "images/z23_z22_drug_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)

shared <- Vennerable::Venn(list(
  "z23" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z23sb_vs_z23nosb"]]),
  "z22" = rownames(hs_macr_sig[["deseq"]][["downs"]][["z22sb_vs_z22nosb"]])))
pp(file = "images/z23_z22_drug_venn_down.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)
```

Note: I am settig the x and y-axis boundaries by allowing the plotter
to pick its own axis the first time, writing down the ranges I
observe, and then setting them to the largest of the pair.  It is
therefore possible that I missed one or more genes which lies outside
that range.

The previous plotted contrasts sought to show changes between the two
strains z2.3 and z2.2.  Conversely, the previous volcano plots seek to
directly compare each strain before/after drug treatment.

# LRT of the Human Macrophage

A slightly different tack to examine the data is to perform a
likelihood ratio test in order to look for trends which are shared
among genes when examining different conditions in the data.

```{r}
tmrc2_lrt_strain_drug <- deseq_lrt(hs_macr, interactor_column = "drug",
                                   interest_column = "macrophagezymodeme",
                                   factors = c("drug", "macrophagezymodeme"))
tmrc2_lrt_strain_drug[["cluster_data"]][["plot"]]
```

# Parasite

Let us consider for a moment differences among the parasite
transcriptomes for the samples which were not drug treated.

One thing I did in the initial implementation of this document was to
repeat the variable 'up_genes' for each comparison; I think this time
I will make a different variable for each comparison so I can play
with them a little further.

```{r}
comparison <- "z23_vs_z22"
lp_macrophage_de <- all_pairwise(lp_macrophage_nosb,
                                 model_svs = "svaseq", filter = TRUE)
tmrc2_parasite_keepers <- list(
  "z23_vs_z22" = c("z23", "z22"))
lp_macrophage_table <- combine_de_tables(
  lp_macrophage_de, keepers = tmrc2_parasite_keepers,
  excel = glue("analyses/macrophage_de/de_tables/parasite_infection_de-v{ver}.xlsx"))
lp_macrophage_sig <- extract_significant_genes(
  lp_macrophage_table,
  excel = glue("analyses/macrophage_de/sig_tables/parasite_sig-v{ver}.xlsx"))

lp_macrophage_table[["plots"]][[comparison]][["deseq_vol_plots"]]
lp_macrophage_table[["plots"]][[comparison]][["deseq_ma_plots"]]

up_genes_z23z22 <- lp_macrophage_sig[["deseq"]][["ups"]][[comparison]]
dim(up_genes_z23z22)
down_genes_z23z22 <- lp_macrophage_sig[["deseq"]][["downs"]][[comparison]]
dim(down_genes_z23z22)
```

```{r parasite_volcano}
lp_z23sb_vs_z22sb_volcano <- plot_volcano_de(
  table = lp_macrophage_table[["data"]][["z23_vs_z22"]],
  fc_col = "deseq_logfc", p_col = "deseq_adjp",
  shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
plotly::ggplotly(lp_z23sb_vs_z22sb_volcano[["plot"]])
lp_z23sb_vs_z22sb_volcano[["plot"]]
```

## Ontology search

An important note, I recently added a minimum crossreference argument
(defaulting to 40 genes), which causes lots of comparisons to fail for
poorly annotated genomes (like panamensis.)  Thus, I am relaxing that
constraint for these searches.

```{r}
lp_lengths <- all_lp_annot[, c("gid", "annot_cds_length")]
colnames(lp_lengths)  <- c("ID", "length")

up_goseq <- simple_goseq(up_genes_z23z22, go_db = lp_go,
                         length_db = lp_lengths, min_xref = 15)
## View categories over represented in the 2.3 samples
up_goseq[["pvalue_plots"]][["bpp_plot_over"]]
down_goseq <- simple_goseq(down_genes_z23z22, go_db = lp_go,
                           length_db = lp_lengths, min_xref = 15)
## View categories over represented in the 2.2 samples
down_goseq[["pvalue_plots"]][["bpp_plot_over"]]

created <- dir.create(glue("analyses/macrophage_de/goseq_parasite"))
written_goseq <- write_goseq_data(
  up_goseq,
  excel = glue("analyses/macrophage_de/goseq_parasite/lp_macrophage_increased_z2.3_goseq-v{ver}.xlsx"))
written_goseq <- write_goseq_data(
  down_goseq,
  excel = glue("analyses/macrophage_de/goseq_parasite/lp_macrophage_increased_z2.2_goseq-v{ver}.xlsx"))
```

# GSVA

Note: The following block assumes one is able to download a fresh copy
of msigDB, which I am not sure is possible within the constraints of a
container (I mean it is trivial to do, but I am not sure if it is ok
due to licensing).  However, Broad provides a data package of a msigdb
release.  As a result, the following block will be repeated using that.

```{r, eval=FALSE}
hs_infected <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
hs_gsva_c2 <- simple_gsva(hs_infected)
hs_gsva_c2_meta <- get_msigdb_metadata(hs_gsva_c2, msig_xml = "reference/msigdb_v7.2.xml")
hs_gsva_c2_sig <- get_sig_gsva_categories(
  hs_gsva_c2_meta,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c2_sig.xlsx"))
hs_gsva_c2_sig[["raw_plot"]]

hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")
hs_gsva_c7_meta <- get_msigdb_metadata(hs_gsva_c7, msig_xml = "reference/msigdb_v7.2.xml")
hs_gsva_c7_sig <- get_sig_gsva_categories(
  hs_gsva_c7,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c7_sig.xlsx"))
hs_gsva_c7_sig[["raw_plot"]]
```

## Repeat using the GSVAdata package.

```{r}
hs_infected <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
hs_gsva_c2 <- simple_gsva(hs_infected)
##hs_gsva_c2_meta <- get_msigdb_metadata(hs_gsva_c2, msig_xml="reference/msigdb_v7.2.xml")
hs_gsva_c2_sig <- get_sig_gsva_categories(
  hs_gsva_c2,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c2_sig.xlsx"))
hs_gsva_c2_sig[["raw_plot"]]

hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")
##hs_gsva_c7_meta <- get_msigdb_metadata(hs_gsva_c7, msig_xml="reference/msigdb_v7.2.xml")
hs_gsva_c7_sig <- get_sig_gsva_categories(
  hs_gsva_c7,
  excel = glue("analyses/macrophage_de/gsva/hs_macrophage_gsva_c7_sig.xlsx"))
hs_gsva_c7_sig[["raw_plot"]]
```

# Try out a new tool

Two reasons: Najib loves him some PCA, this uses wikipathways, which is something I think is neat.

Ok, I spent some time looking through the code and I have some
problems with some of the design decisions.

Most importantly, it requires a data.frame() which has the following format:

1.  No rownames, instead column #1 is the sample ID.
2.  Columns 2-m are the categorical/survival/etc metrics.
3.  Columns m-n are 1 gene-per-column with log2 values.

But when I think about it I think I get the idea, they want to be able to do modelling stuff
more easily with response factors.

```{r}
library(pathwayPCA)
library(rWikiPathways)

downloaded <- downloadPathwayArchive(organism = "Homo sapiens", format = "gmt")
data_path <- system.file("extdata", package = "pathwayPCA")
wikipathways <- read_gmt(paste0(data_path, "/wikipathways_human_symbol.gmt"),
                         description = TRUE)

expt <- subset_expt(hs_macrophage, subset = "macrophagetreatment!='uninf'") %>%
  subset_expt(subset = "macrophagetreatment!='uninf_sb'")
expt <- set_expt_conditions(expt, fact = "macrophagezymodeme")
symbol_column <- "hgnc_symbol"
symbol_vector <- fData(expt)[[symbol_column]]
names(symbol_vector) <- rownames(fData(expt))
symbol_df <- as.data.frame(symbol_vector)

assay_df <- merge(symbol_df, as.data.frame(exprs(expt)), by = "row.names")
assay_df[["Row.names"]] <- NULL
rownames(assay_df) <- make.names(assay_df[["symbol_vector"]], unique = TRUE)
assay_df[["symbol_vector"]] <- NULL
assay_df <- as.data.frame(t(assay_df))
assay_df[["SampleID"]] <- rownames(assay_df)
assay_df <- dplyr::select(assay_df, "SampleID", everything())

factor_df <- as.data.frame(pData(expt))
factor_df[["SampleID"]] <- rownames(factor_df)
factor_df <- dplyr::select(factor_df, "SampleID", everything())
factor_df <- factor_df[, c("SampleID", factors)]

tt <- CreateOmics(
  assayData_df = assay_df,
  pathwayCollection_ls = wikipathways,
  response = factor_df,
  respType = "categorical",
  minPathSize = 5)

super <- AESPCA_pVals(
  object = tt,
  numPCs = 2,
  parallel = FALSE,
  numCores = 8,
  numReps = 2,
  adjustment = "BH")
```

# Evaluating a log2FC barplot

Figure 2E is now comprised of a plot which shows log2FC values with
error bars for selected genes and seeks to show differences between
2.3/uninfected and 2.2/uninfected.

Here is the table Olga used to generate it:

I went looking in the xlsx files produced in 202405 and found that
these are the log2FC values and standard errors produced by DESeq2.

It should be noted that in my most recent version of these analyses,
these numbers did shift slightly.  I am looking into that now.

* Data witout drug

** 2.3 vs Uninfected MØ            2.2 vs Uninfected MØ

| Gene | Mean   | SEM      | n | Mean  | SEM     |n |
----------------------------------------------------
|IFI27 |  7.224 |  0.5662  |6  | 2.702 |  0.5669 | 6|
|RSAD2 |  6.29  |  0.7312  |6  | 1.623 |  0.7303 | 6|
|CCL8  |  6.225 |  0.928   |6  | -0.314|  0.941  | 6|
|IFI44L|  5.895 |  0.612   |6  | 2.06  |  0.611  | 6|
|OASL  |  4.726 |  0.4974  |6  | 1.392 |  0.4973 | 6|
|USP18 |  3.644 |  0.483   |6  | 0.999 |  0.4826 | 6|
|IDO1  |  7.145 |  1.107   |6  | 1.257 |  1.141  | 6|
|IDO2  |  3.935 |  1.3     |6  | 2.557 |  1.341  | 6|
|KYNU  |  1.07  |  0.2186  |6  | 0.0207|  0.2184 | 6|
|AHR   | 0.9382 | 0.2236   |6  | 0.5032|  0.2239 | 6|
|IL4I1 |  2.593 |  0.4623  |6  | 0.039 |  0.4618 | 6|
|SOD2  |  2.76  |  0.349   |6  | 0.4241|  0.3528 | 6|
|NOTCH1|  0.7572|  0.275   |6  | 1.495 |  0.2744 | 6|
|DLL1  |  0.8268|  0.5285  |6  | 3.455 |  0.5228 | 6|
|DLL4  |  1.116 |  0.737   |6  | 4.243 |  0.71   | 6|
|HES1  | -0.0183| 0.8599   |6  | 6.536 |  0.7973 | 6|
|HEY1  |  0.5533| 0.5789   |6  | 4.181 |  0.6273 | 6|

Ok, I think I found a problem: The NOTCH1 value is actually the
adjusted p-value.

* Transporters without drug

** 2.3 vs Uninfected MØ            2.2 vs Uninfected MØ

| Gene | Mean    |   SEM  | n|  Mean   | SEM   | n|
-------------------------------------------------------
|ABCB1 |  -2.354 | 0.442  | 6|   -0.406|  0.431| 6|
|ABCG4 |  -3.715 | 0.648  | 6|   -0.653|  0.630| 6|
|ABCB5 |  -1.192 | 0.380  | 6|   1.351 |  0.363| 6|
|ABCA9 |  1.880  | 0.648  | 6|   3.444 |  0.637| 6|
|ABCC2 |  0.454  | 0.321  | 6|   1.818 |  0.314| 6|
|AQP2  |  -1.191 | 0.529  | 6|   0.745 |  0.514| 6|
|AQP3  |  -0.940 | 0.402  | 6|   0.431 |  0.395| 6|

* Transporters with drug

** 2.3 vs Uninfected MØ            2.2 vs Uninfected MØ

|Gene  | Mean   |    SEM | n| Mean  |  SEM   | n |
---------------------------------------------------
|ABCB1 |  -0.697|  0.349 | 6| -1.255|  0.337 |  6|
|ABCG4 |  1.231 |  0.503 | 6| 0.547 |  0.484 |  6|
|AQP2  |  0.816 |  0.399 | 6| 0.043 |  0.387 |  6|
|AQP3  |  -1.286|  0.320 | 6| -1.613|  0.309 |  6|
|AQP8  |  0.634 |  0.370 | 6| 0.943 |  0.365 |  6|

Let us now see if I can recapitulate the plot...

```{r}
nodrug_contrasts <- c("z23nosb_vs_uninf", "z22nosb_vs_uninf")
genes_no_drug <- c("IFI27", "RSAD2", "CCL8", "IFI44L", "OASL", "USP18", "IDO1", "IDO2", "KYNU", "AHR", "IL4I1", "SOD2", "NOTCH1", "DLL1", "DLL4", "HES1", "HEY1")
transporters_no_drug <- c("ABCB1", "ABCG4", "ABCB5", "ABCA9", "ABCC2", "AQP2", "AQP3")
drug_contrasts <- c("z23sb_vs_sb", "z22sb_vs_sb")
transporters_drug <- c("ABCB1", "ABCG4", "AQP2", "AQP3", "AQP8")
```

These values came out of the data structure called 'hs_macr_table'

```{r}
z23nosb_uninf_values <- hs_macr_table[["data"]][["z23nosb_vs_uninf"]]
gene_idx <- z23nosb_uninf_values[["hgnc_symbol"]] %in% genes_no_drug
nodrug_rows <-  z23nosb_uninf_values[gene_idx, ]
rownames(nodrug_rows) <- nodrug_rows[["hgnc_symbol"]]
z23_nodrug_values <- nodrug_rows[, c("deseq_logfc", "deseq_lfcse")]
z23_nodrug_values

z22nosb_uninf_values <- hs_macr_table[["data"]][["z22nosb_vs_uninf"]]
gene_idx <- z22nosb_uninf_values[["hgnc_symbol"]] %in% genes_no_drug
nodrug_rows <-  z22nosb_uninf_values[gene_idx, ]
rownames(nodrug_rows) <- nodrug_rows[["hgnc_symbol"]]
z22_nodrug_values <- nodrug_rows[, c("deseq_logfc", "deseq_lfcse")]
z22_nodrug_values

z23_nodrug_values[["state"]] <- "z23_vs_uninfected"
z22_nodrug_values[["state"]] <- "z22_vs_uninfected"
plot_df <- rbind.data.frame(as.data.frame(z23_nodrug_values), as.data.frame(z22_nodrug_values))
plot_df[["gene"]] <- rownames(plot_df)

## I just realized that this is actually just a comparison of z23/z22
## we should just take the adjusted p-values from that contrast for this.
z23_z22_comparison <- hs_macr_table[["data"]][["z23nosb_vs_z22nosb"]]
nodrug_rows <- z23_z22_comparison[gene_idx, ]
nodrug_pvalues <- nodrug_rows[, c("deseq_p", "deseq_adjp")]
rownames(nodrug_pvalues) <- nodrug_rows[["hgnc_symbol"]]
nodrug_pvalues

ggplot(plot_df, aes(x = gene, y = deseq_logfc, fill = state)) +
  geom_bar(position = position_dodge(), stat = "identity") +
  geom_errorbar(aes(ymin = deseq_logfc - deseq_lfcse,
                    ymax = deseq_logfc + deseq_lfcse),
                width = 0.2, position = position_dodge(0.9)) +
  scale_fill_manual(values = c("#1B9E77", "#7570B3")) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

comparison <- c("z23_vs_uninfected", "z22_vs_uninfected")
comparisons <- rep(list(comparison), nrow(plot_df) / 2)
ggplot(plot_df, aes(x = gene, y = deseq_logfc, fill = state, add = deseq_lfcse, facet.by = "state")) +
  geom_bar(position = position_dodge(), stat = "identity") +
  geom_errorbar(aes(ymin = deseq_logfc - deseq_lfcse,
                    ymax = deseq_logfc + deseq_lfcse),
                width = 0.2, position = position_dodge(0.9)) +
  stat_compare_means() +
  stat_compare_means(comparisons = comparisons, label.y = rownames(z23_nodrug_values)) +
  scale_fill_manual(values = c("#1B9E77", "#7570B3")) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5))

```

Excellent, the values now match up.  Now I ust need to figure out why
the stupid hgnc IDs got lost...  I can see them in the hs_annot data
structure, so I must have messed up when I regenered the input to the
de.  Ok, I got to the same starting point now with identical values.
As soon as I did that, I looked at the resulting plot and realized
that we are actually just comparing z23 / z22.

Here is why: the plot as it stands is a comparison of the log2FC
values of the following two contrasts: z23/uninfected and
z22/uninfected; stated differently, this is (z23/uninf)/(z22/uninf)
which of course cancels out to just z23/z22.

Therefore it is much more parsimonious to just use the values from
z23/z22.  I swear I have gone through this exact exercise on so so
many occasions in the past it is terrible.

```{r}
wanted_genes <- c("IFI27", "RSAD2", "CCL8", "IFI44L", "OASL",
                  "USP18", "IDO1", "IDO2", "KYNU", "AHR", "IL4I1",
                  "SOD2", "NOTCH1", "DLL1", "DLL4", "HES1", "HEY1")
ggsignif_plot <- ggsignif_paired_genes(
  hs_macr, conditions = c("inf_z23", "inf_z22"), genes = wanted_genes)
ggsignif_plot
```

```{r}
pander::pander(sessionInfo())
message("This is hpgltools commit: ", get_git_commit())
tmp <- saveme(filename = savefile)
```

```{r loadme_after, eval=FALSE}
tmp <- loadme(filename = savefile)
```
devtools::load_all('~/hpgltools')
