Given this stricter filter, I think no genes pass in the genome
comparisons.
Location contrasts
with genes removed/kept
We will now repeat the above tasks seeking location differences
instead of genotype; essentially I copy/pasted the above with
s/genotype/location/g.
location_tables_full <- combine_de_tables(
location_de, keepers = location_keepers, label_column = label_column,
excel = glue("17full_location_contrasts_tx/location_full_tables-v{ver}.xlsx"))
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_het_dlgn_vs_p08_het_retina 1138 1074 1324 1127 1027 976
## 2 p15_het_dlgn_vs_p15_het_retina 1001 1314 1149 1479 969 1110
## 3 p08_ko_dlgn_vs_p08_ko_retina 960 914 1087 996 824 994
## 4 p15_ko_dlgn_vs_p15_ko_retina 882 1107 1042 1187 787 888
## 5 p08_het_scn_vs_p08_het_retina 1410 1414 1685 1516 1312 1239
## 6 p15_het_scn_vs_p15_het_retina 1383 1460 1641 1643 1362 1371
## 7 p08_ko_scn_vs_p08_ko_retina 1181 1184 1391 1265 1117 1103
## 8 p15_ko_scn_vs_p15_ko_retina 939 1135 1156 1346 976 1018
## 9 p08_het_dlgn_vs_p08_het_scn 443 483 513 612 402 478
## 10 p15_het_dlgn_vs_p15_het_scn 1127 1422 1231 1619 1268 1421
## 11 p08_ko_dlgn_vs_p08_ko_scn 446 483 501 573 529 746
## 12 p15_ko_dlgn_vs_p15_ko_scn 1237 1303 1427 1448 1267 1329
## Plot describing unique/shared genes in a differential expression table.
location_sig_full <- extract_significant_genes(
location_tables_full, according_to = "deseq",
excel = glue("17full_location_contrasts_tx/location_full_sig-v{ver}.xlsx"))
location_sig_full
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## dr_p08_het 1138 1074
## dr_p15_het 1001 1314
## dr_p08_ko 960 914
## dr_p15_ko 882 1107
## sr_p08_het 1410 1414
## sr_p15_het 1383 1460
## sr_p08_ko 1181 1184
## sr_p15_ko 939 1135
## ds_p08_het 443 483
## ds_p15_het 1127 1422
## ds_p08_ko 446 483
## ds_p15_ko 1237 1303
location_full_upset <- upsetr_sig(location_sig_full)
location_full_intersects <- write_upset_groups(
location_full_upset,
excel = "excel/17full_location_contrasts_tx/location_full_gene_groups.xlsx")
## Deleting the file excel/17full_location_contrasts_tx/location_full_gene_groups.xlsx before writing the tables.
location_tables <- list()
location_sig <- list()
location_gp <- list()
location_cp <- list()
for (k in seq_along(location_keepers)) {
name <- names(location_keepers)[k]
message("Examining ", name)
keeper <- location_keepers[name]
includes <- location_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- location_inclusions[[include_df_name]]
includes <- location_inclusions[[include_name]]
summary(rownames(location_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
include_filename <- glue("18location_contrasts_tx/{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
include_sig_filename <- glue("18location_contrasts_tx/{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
location_tables[[name]] <- combine_de_tables(
location_de, extra_annot = include_df,
keepers = keeper, label_column = label_column,
excel = include_filename, wanted_genes = includes)
print(location_tables[[name]])
location_sig[[name]] <- extract_significant_genes(
location_tables[[name]], according_to = "deseq",
excel = include_sig_filename)
print(location_sig[[name]])
num_rows <- nrow(location_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(location_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
if (num_rows > 10) {
location_gp[[name]] <- all_gprofiler(location_sig[[name]], species = "mmusculus")
location_cp[[name]] <- all_cprofiler(
location_sig[[name]], location_tables[[name]],
orgdb = "org.Mm.eg.db", go_level = go_level, orgdb_from = orgdb_from,
max_groupsize = max_groupsize, organism = "mouse")
cp_written <- write_all_cp(location_cp[[name]], prefix = "19")
gp_written <- write_all_gp(location_gp[[name]], prefix = "19")
}
}
## Examining dr_p08_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_het_dlgn_vs_p08_het_retina 110 96 124 90 109 81
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## dr_p08_het 110 96
## There are 206 significant up and down genes.
## There are 48 genes deemed significant out of 310.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p08_het_up.xlsx before writing the tables.
## There are 44 genes deemed significant out of 310.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.27% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p08_het_down.xlsx before writing the tables.
## Examining dr_p15_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_dlgn_vs_p15_het_retina 253 55 276 55 216 53
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## dr_p15_het 253 55
## There are 308 significant up and down genes.
## There are 91 genes deemed significant out of 495.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p15_het_up.xlsx before writing the tables.
## There are 23 genes deemed significant out of 495.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.22% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p15_het_down.xlsx before writing the tables.
## Examining dr_p08_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_ko_dlgn_vs_p08_ko_retina 258 67 276 67 210 72
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## dr_p08_ko 258 67
## There are 325 significant up and down genes.
## There are 122 genes deemed significant out of 440.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.6% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p08_ko_up.xlsx before writing the tables.
## There are 28 genes deemed significant out of 440.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.6% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p08_ko_down.xlsx before writing the tables.
## Examining dr_p15_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_dlgn_vs_p15_ko_retina 334 96 359 100 281 93
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## dr_p15_ko 334 96
## There are 430 significant up and down genes.
## There are 125 genes deemed significant out of 598.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in reality P-values are less than
## 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p15_ko_up.xlsx before writing the tables.
## There are 38 genes deemed significant out of 598.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in reality P-values are less than
## 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.59% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.52% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_dr_p15_ko_down.xlsx before writing the tables.
## Examining sr_p08_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_het_scn_vs_p08_het_retina 80 111 90 105 85 100
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## sr_p08_het 80 111
## There are 191 significant up and down genes.
## There are 31 genes deemed significant out of 224.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_sr_p08_het_up.xlsx before writing the tables.
## There are 50 genes deemed significant out of 224.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.45% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_sr_p08_het_down.xlsx before writing the tables.
## Examining sr_p15_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_scn_vs_p15_het_retina 24 69 26 64 23 70
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## sr_p15_het 24 69
## There are 93 significant up and down genes.
## There are 9 genes deemed significant out of 86.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## Deleting the file excel/all_cp_sr_p15_het_up.xlsx before writing the tables.
## There are 29 genes deemed significant out of 86.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## Deleting the file excel/all_cp_sr_p15_het_down.xlsx before writing the tables.
## Examining sr_p08_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_ko_scn_vs_p08_ko_retina 112 76 122 71 114 60
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## sr_p08_ko 112 76
## There are 188 significant up and down genes.
## There are 51 genes deemed significant out of 219.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_sr_p08_ko_up.xlsx before writing the tables.
## There are 32 genes deemed significant out of 219.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.46% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_sr_p08_ko_down.xlsx before writing the tables.
## Examining sr_p15_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_scn_vs_p15_ko_retina 11 20 10 20 11 16
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## sr_p15_ko 11 20
## There are 31 significant up and down genes.
## There are 6 genes deemed significant out of 28.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## Deleting the file excel/all_cp_sr_p15_ko_up.xlsx before writing the tables.
## There are 6 genes deemed significant out of 28.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## Deleting the file excel/all_cp_sr_p15_ko_down.xlsx before writing the tables.
## Examining ds_p08_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_het_dlgn_vs_p08_het_scn 39 6 39 7 32 7
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## ds_p08_het 39 6
## There are 45 significant up and down genes.
## There are 23 genes deemed significant out of 103.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.94% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p08_het_up.xlsx before writing the tables.
## There are 1 genes deemed significant out of 103.
## There are 1 entries which match the search string: Mus musculus.
## No gene sets have size between 5 and 500 ...
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 1 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## no term enriched under specific pvalueCutoff...
## --> No gene can be mapped....
## --> Expected input gene ID: 66925,234309,239017,171210,66681,18640
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.94% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## --> No gene can be mapped....
## --> Expected input gene ID: 71623,69769,11813,215335,194237,54613
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## No gene sets have size between 5 and 2000 ...
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.91% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p08_het_down.xlsx before writing the tables.
## Examining ds_p15_het
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_dlgn_vs_p15_het_scn 611 2 610 3 558 2
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## ds_p15_het 611 2
## There are 613 significant up and down genes.
## There are 272 genes deemed significant out of 441.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.67% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 2 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.76% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.67% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.67% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.67% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p15_het_up.xlsx before writing the tables.
## Error in sig_genes_namedf[[orgdb_to]] : subscript out of bounds
## Examining ds_p08_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p08_ko_dlgn_vs_p08_ko_scn 115 3 112 3 87 6
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## ds_p08_ko 115 3
## There are 118 significant up and down genes.
## There are 59 genes deemed significant out of 257.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 1 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p08_ko_up.xlsx before writing the tables.
## There are 2 genes deemed significant out of 257.
## There are 1 entries which match the search string: Mus musculus.
## No gene sets have size between 5 and 500 ...
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 1 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## no term enriched under specific pvalueCutoff...
## --> No gene can be mapped....
## --> Expected input gene ID: 243996,66198,331026,11529,106557,18655
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## --> No gene can be mapped....
## --> Expected input gene ID: 11732,73836,56043,11864,193813,18802
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## No gene sets have size between 5 and 2000 ...
## --> return NULL...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.39% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p08_ko_down.xlsx before writing the tables.
## Examining ds_p15_ko
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_dlgn_vs_p15_ko_scn 755 2 796 1 641 0
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## ds_p15_ko 755 2
## There are 757 significant up and down genes.
## There are 321 genes deemed significant out of 577.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.03% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 6 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.14% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : All values in the stats vector are greater than zero and
## scoreType is "std", maybe you should switch to scoreType = "pos".
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.03% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.03% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (5.03% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_ds_p15_ko_up.xlsx before writing the tables.
## Error in sig_genes_namedf[[orgdb_to]] : subscript out of bounds
Print out all the plots in a separate block.
for (k in seq_along(location_keepers)) {
name <- names(location_keepers)[k]
message("Examining ", name)
keeper <- location_keepers[name]
includes <- location_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- location_inclusions[[include_df_name]]
includes <- location_inclusions[[include_name]]
summary(rownames(location_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
num_rows <- nrow(location_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(location_sig[[name]][["deseq"]][["downs"]][[name]])
nrow(location_sig[[name]][["deseq"]][["ups"]][[name]])
nrow(location_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
num_objects <- length(location_cp[[name]])
if (num_objects == 0) {
warning("Something went wrong in all_cprofiler.")
} else {
upp <- which(grepl(x = names(location_cp[[name]]), pattern = "_up$"))
downp <- which(grepl(x = names(location_cp[[name]]), pattern = "_down$"))
if (length(upp) > 0) {
mf_sig <- location_cp[[name]][[upp]][["go_data"]][["MF_enrich"]]
cc_sig <- location_cp[[name]][[upp]][["go_data"]][["CC_enrich"]]
bp_sig <- location_cp[[name]][[upp]][["go_data"]][["BP_enrich"]]
mf_plots_up <- plot_enrichresult(mf_sig, showCategory = go_categories)
mf_tree_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_mf_sig_tree.pdf")
pp(file = mf_tree_up_filename)
try(print(mf_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
mf_bar_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_mf_sig_bar.pdf")
pp(file = mf_bar_up_filename)
try(print(mf_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
cc_plots_up <- plot_enrichresult(cc_sig, showCategory = go_categories)
cc_tree_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_cc_sig_tree.pdf")
pp(file = cc_tree_up_filename)
try(print(cc_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
cc_bar_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_cc_sig_bar.pdf")
pp(file = cc_bar_up_filename)
try(print(cc_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_up <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_tree_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_bp_sig_tree.pdf")
pp(file = bp_tree_up_filename)
try(print(bp_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_up <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_bar_up_filename <- glue("19clusterProfiler_plots_tx/{name}_up_bp_sig_bar.pdf")
pp(file = bp_bar_up_filename)
try(print(bp_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
}
if (length(downp) > 0) {
mf_sig <- location_cp[[name]][[downp]][["go_data"]][["MF_enrich"]]
cc_sig <- location_cp[[name]][[downp]][["go_data"]][["CC_enrich"]]
bp_sig <- location_cp[[name]][[downp]][["go_data"]][["BP_enrich"]]
mf_plots_down <- plot_enrichresult(mf_sig, showCategory = go_categories)
mf_tree_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_mf_sig_tree.pdf")
pp(file = mf_tree_down_filename)
try(print(mf_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
mf_bar_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_mf_sig_bar.pdf")
pp(file = mf_bar_down_filename)
try(print(mf_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
cc_plots_down <- plot_enrichresult(cc_sig, showCategory = 12)
cc_tree_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_cc_sig_tree.pdf")
pp(file = cc_tree_down_filename)
try(print(cc_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
cc_bar_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_cc_sig_bar.pdf")
pp(file = cc_bar_down_filename)
try(print(cc_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_down <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_tree_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_bp_sig_tree.pdf")
pp(file = bp_tree_down_filename)
try(print(bp_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_down <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_bar_down_filename <- glue("19clusterProfiler_plots_tx/{name}_down_bp_sig_bar.pdf")
pp(file = bp_bar_down_filename)
try(print(bp_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
}
}
}
## Examining dr_p08_het
## There are 206 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining dr_p15_het
## There are 308 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining dr_p08_ko
## There are 325 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in max(idx1, idx2): no non-missing arguments to max; returning -Inf
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in max(idx1, idx2): no non-missing arguments to max; returning -Inf
## NULL
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining dr_p15_ko
## There are 430 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining sr_p08_het
## There are 191 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining sr_p15_het
## There are 93 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining sr_p08_ko
## There are 188 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining sr_p15_ko
## There are 31 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining ds_p08_het
## There are 45 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining ds_p15_het
## There are 613 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining ds_p08_ko
## There are 118 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining ds_p15_ko
## There are 757 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
Colenso sent a specific query of interest, comparing SCN vs. Retinas
at p08 in the heterozygotes including a set of genes of particular
interest. Perhaps I can use some of these as markers to quality control
my work in the future?
Here are the genes:
Opn4, Eomes, Trpc7, Oprm1, Nr4a3, Tbx20, Irx6, AW551984, Pcdh19,
Adcyap1, Baiap3, Chl1, Grin3a, Igf1, Gria1, Grin2d, Grin3a, Chrna6,
Chrna3, Htr5a, Htr2a, Htr7, Irx4, PlxnC1, Sema6d, Sema4f, Sema4a,
Sema6b, Lrrc4b, Lrrc58, Lrrc3b, Wnt4, Wnt9b, Ctxn3, Tenm1, Gna14, Rgs4,
Rgs6, Rgs5
table_input <- location_tables[["sr_p08_het"]]
table_name <- "sr_p08_het"
table <- table_input[["data"]][[table_name]]
interesting_genes <- c("Opn4", "Eomes", "Trpc7", "Oprm1", "Nr4a3", "Tbx20",
"Irx6", "AW551984", "Pcdh19", "Adcyap1r1", "Baiap3",
"Chl1", "Grin3a", "Igf1", "Gria1", "Grin2d", "Grin3a",
"Chrna6", "Chrna3", "Htr5a", "Htr2a", "Htr7", "Irx4",
"PlxnC1", "Sema6d", "Sema4f", "Sema4a", "Sema6b", "Lrrc4b",
"Lrrc58", "Lrrc3b", "Wnt4", "Wnt9b", "Ctxn3", "Tenm1", "Gna14",
"Rgs4", "Rgs6", "Rgs5", "Pou4f2", "Chrnb3", "Bcan")
sr_p08_het_volcano <- plot_volcano_condition_de(
table, table_name, fc_col = "deseq_logfc", p_col = "deseq_adjp",
color_low = colors[["het_retina"]], color_high = colors[["het_scn"]],
label_column = "mgi_symbol", label = interesting_genes, alpha = 1.0,
size = 4)
pp(file = "20location_ma_volcano_tx/sr_p08_het_volcano.pdf", width = 9, height = 9)
sr_p08_het_volcano[["plot"]]
plotted <- dev.off()
sr_p08_het_volcano[["plot"]]
sr_p08_het_ma <- plot_ma_condition_de(
table, table_name, expr_col = "deseq_basemean", fc_col = "deseq_logfc",
color_low = colors[["het_retina"]], color_high = colors[["het_scn"]],
p_col = "deseq_adjp", label_column = "mgi_symbol", label = interesting_genes, outline = outline)
## The column: mgi_symbol is not in the data, using rownames.
pp(file = "20location_ma_volcano_tx/sr_p08_het_ma.pdf", width = 9, height = 9)
sr_p08_het_ma[["plot"]]
plotted <- dev.off()
sr_p08_het_ma[["plot"]]
And time
time_tables_full <- combine_de_tables(
time_de, keepers = time_keepers,
label_column = label_column,
excel = glue("25full_contrasts_time_tx/full_tables-v{ver}.xlsx"))
## Looking for subscript invalid names, end of extract_keepers.
time_sig_full <- extract_significant_genes(
time_tables_full, according_to = "deseq",
excel = glue("25full_contrasts_time_tx/full_sig-v{ver}.xlsx"))
time_tables <- list()
time_sig <- list()
time_gp <- list()
time_cp <- list()
for (k in seq_along(time_keepers)) {
name <- names(time_keepers)[k]
message("Examining ", name)
keeper <- time_keepers[name]
includes <- time_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- time_inclusions[[include_df_name]]
includes <- time_inclusions[[include_name]]
summary(rownames(time_sig_full[["deseq"]][["ups"]][[name]]) %in% includes)
include_filename <- glue("26time_contrasts_tx/{name}_including_wt_{lfc_cutoff}_decreased_table-v{ver}.xlsx")
include_sig_filename <- glue("26time_contrasts_tx/{name}_including_wt_{lfc_cutoff}_decreased_sig-v{ver}.xlsx")
time_tables[[name]] <- combine_de_tables(
time_de, extra_annot = include_df,
keepers = keeper, label_column = label_column,
excel = include_filename, wanted_genes = includes)
print(time_tables[[name]])
time_sig[[name]] <- extract_significant_genes(
time_tables[[name]], according_to = "deseq",
excel = include_sig_filename)
print(time_sig[[name]])
num_rows <- nrow(time_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(time_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
if (num_rows > 10) {
time_gp[[name]] <- all_gprofiler(time_sig[[name]], species = "mmusculus")
gp_written <- write_all_gp(time_gp[[name]])
time_cp[[name]] <- all_cprofiler(
time_sig[[name]], time_tables[[name]], orgdb = "org.Mm.eg.db", organism = "mouse",
orgdb_from = orgdb_from, go_level = go_level, max_groupsize = max_groupsize)
cp_written <- write_all_cp(time_cp[[name]], prefix = "27")
gp_written <- write_all_gp(time_gp[[name]], prefix = "27")
}
}
## Examining t_het_dlgn
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_dlgn_vs_p08_het_dlgn 281 9 255 9 197 8
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_het_dlgn 281 9
## There are 290 significant up and down genes.
## There are 113 genes deemed significant out of 461.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 6 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.3% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_het_dlgn_up.xlsx before writing the tables.
## There are 4 genes deemed significant out of 461.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 3 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.3% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : For some of the pathways the P-values were likely overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (3.25% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_het_dlgn_down.xlsx before writing the tables.
## Examining t_ko_dlgn
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_dlgn_vs_p08_ko_dlgn 357 89 390 87 338 73
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_ko_dlgn 357 89
## There are 446 significant up and down genes.
## There are 158 genes deemed significant out of 697.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 21 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.9% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_ko_dlgn_up.xlsx before writing the tables.
## There are 49 genes deemed significant out of 697.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : There were 25 pathways for which P-values were not
## calculated properly due to unbalanced (positive and negative) gene-level statistic values. For such pathways pval, padj, NES, log2err are set
## to NA. You can try to increase the value of the argument nPermSimple (for example set it nPermSimple = 10000)
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some of the pathways the P-values were likely
## overestimated. For such pathways log2err is set to NA.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.9% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (1.87% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_ko_dlgn_down.xlsx before writing the tables.
## Examining t_het_retina
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_retina_vs_p08_het_retina 30 103 30 116 24 102
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_het_retina 30 103
## There are 133 significant up and down genes.
## There are 17 genes deemed significant out of 248.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_het_retina_up.xlsx before writing the tables.
## There are 38 genes deemed significant out of 248.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_het_retina_down.xlsx before writing the tables.
## Examining t_ko_retina
## Looking for subscript invalid names, end of extract_keepers.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_retina_vs_p08_ko_retina 12 136 11 143 9 134
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Plot describing unique/shared genes in a differential expression table.
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_ko_retina 12 136
## There are 148 significant up and down genes.
## There are 6 genes deemed significant out of 227.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_ko_retina_up.xlsx before writing the tables.
## There are 45 genes deemed significant out of 227.
## There are 1 entries which match the search string: Mus musculus.
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## no term enriched under specific pvalueCutoff...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
## preparing geneSet collections...
## GSEA analysis...
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.88% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## leading edge analysis...
## done...
## Deleting the file excel/all_cp_t_ko_retina_down.xlsx before writing the tables.
## Examining t_het_scn
## Looking for subscript invalid names, end of extract_keepers.
## The result table is too small for meaningful comparisons.
## The first table has only: 17.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_het_scn_vs_p08_het_scn 0 1 0 1 0 1
## Only t_het_scn_down has information, cannot create an UpSet.
## Plot describing unique/shared genes in a differential expression table.
## NULL
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_het_scn 0 1
## There are 1 significant up and down genes.
## Examining t_ko_scn
## Looking for subscript invalid names, end of extract_keepers.
## The result table is too small for meaningful comparisons.
## The first table has only: 8.
## A set of combined differential expression results.
## table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 p15_ko_scn_vs_p08_ko_scn 0 0 0 0 0 0
## Only has information, cannot create an UpSet.
## Plot describing unique/shared genes in a differential expression table.
## NULL
## A set of genes deemed significant according to deseq.
## The parameters defining significant were:
## LFC cutoff: 1 adj P cutoff: 0.05
## deseq_up deseq_down
## t_ko_scn 0 0
## There are 0 significant up and down genes.
Send the plots separately.
for (k in seq_along(time_keepers)) {
name <- names(time_keepers)[k]
message("Examining ", name)
keeper <- time_keepers[name]
includes <- time_inclusions[[name]]
include_name <- paste0("inc_", name)
include_df_name <- paste0("df_", name)
include_df <- time_inclusions[[include_df_name]]
includes <- time_inclusions[[include_name]]
num_rows <- nrow(time_sig[[name]][["deseq"]][["ups"]][[name]]) +
nrow(time_sig[[name]][["deseq"]][["downs"]][[name]])
message("There are ", num_rows, " significant up and down genes.")
num_objects <- length(time_cp[[name]])
if (num_objects == 0) {
warning("Something failed in all_cprofiler.")
} else {
upp <- which(grepl(x = names(time_cp[[name]]), pattern = "_up$"))
downp <- which(grepl(x = names(time_cp[[name]]), pattern = "_down$"))
if (length(upp) > 0) {
mf_sig <- time_cp[[name]][[upp]][["go_data"]][["MF_enrich"]]
cc_sig <- time_cp[[name]][[upp]][["go_data"]][["CC_enrich"]]
bp_sig <- time_cp[[name]][[upp]][["go_data"]][["BP_enrich"]]
mf_plots_up <- plot_enrichresult(mf_sig, showCategory = go_categories)
mf_tree_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_mf_sig_tree.pdf")
pp(file = mf_tree_up_filename)
try(print(mf_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
mf_bar_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_mf_sig_bar.pdf")
pp(file = mf_bar_up_filename)
try(print(mf_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
cc_plots_up <- plot_enrichresult(cc_sig, showCategory = go_categories)
cc_tree_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_cc_sig_tree.pdf")
pp(file = cc_tree_up_filename)
try(print(cc_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
cc_bar_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_cc_sig_bar.pdf")
pp(file = cc_bar_up_filename)
try(print(cc_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_up <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_tree_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_bp_sig_tree.pdf")
pp(file = bp_tree_up_filename)
try(print(bp_plots_up[["tree"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_up <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_bar_up_filename <- glue("28clusterProfiler_plots_tx/{name}_up_bp_sig_bar.pdf")
pp(file = bp_bar_up_filename)
try(print(bp_plots_up[["bar"]]), silent = TRUE)
plotted <- dev.off()
}
if (length(downp) > 0) {
mf_sig <- time_cp[[name]][[downp]][["go_data"]][["MF_enrich"]]
cc_sig <- time_cp[[name]][[downp]][["go_data"]][["CC_enrich"]]
bp_sig <- time_cp[[name]][[downp]][["go_data"]][["BP_enrich"]]
mf_plots_down <- plot_enrichresult(mf_sig, showCategory = go_categories)
mf_tree_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_mf_sig_tree.pdf")
pp(file = mf_tree_down_filename)
try(print(mf_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
mf_bar_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_mf_sig_bar.pdf")
pp(file = mf_bar_down_filename)
try(print(mf_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
cc_plots_down <- plot_enrichresult(cc_sig, showCategory = go_categories)
cc_tree_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_cc_sig_tree.pdf")
pp(file = cc_tree_down_filename)
try(print(cc_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
cc_bar_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_cc_sig_bar.pdf")
pp(file = cc_bar_down_filename)
try(print(cc_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_down <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_tree_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_bp_sig_tree.pdf")
pp(file = bp_tree_down_filename)
try(print(bp_plots_down[["tree"]]), silent = TRUE)
plotted <- dev.off()
bp_plots_down <- plot_enrichresult(bp_sig, showCategory = go_categories)
bp_bar_down_filename <- glue("28clusterProfiler_plots_tx/{name}_down_bp_sig_bar.pdf")
pp(file = bp_bar_down_filename)
try(print(bp_plots_down[["bar"]]), silent = TRUE)
plotted <- dev.off()
}
}
}
## Examining t_het_dlgn
## There are 290 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining t_ko_dlgn
## There are 446 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining t_het_retina
## There are 133 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining t_ko_retina
## There are 148 significant up and down genes.
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Warning in (function (model, data, ...) : Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Arguments in `...` must be used.
## ✖ Problematic argument:
## • by = "Count"
## ℹ Did you misspell an argument name?
## Examining t_het_scn
## There are 1 significant up and down genes.
## Warning: Something failed in all_cprofiler.
## Examining t_ko_scn
## There are 0 significant up and down genes.
## Warning: Something failed in all_cprofiler.
