3_GSEA
Yang & Steve
24/02/2020
Last updated: 2020-02-27
Checks: 6 1
Knit directory: 20190717_Lardelli_RNASeq_Larvae/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 323a5d7 | Yang Dong | 2020-02-27 | Add in library(rWikiPathways) |
| Rmd | e75f1f6 | Yang Dong | 2020-02-27 | fix |
| Rmd | bc39d1c | Yang Dong | 2020-02-27 | Output results |
| Rmd | ae5f031 | Yang Dong | 2020-02-26 | update of wikipathway |
| Rmd | b2d2284 | Yang Dong | 2020-02-25 | Reorganized |
Setup
library(limma)
library(edgeR)
library(AnnotationHub)
library(tidyverse)
library(magrittr)
library(pander)
library(RColorBrewer)
library(ggrepel)
library(swfdr)
library(scales)
library(here)
library(variancePartition)
library(plyr)
library(ggraph)
library(tidygraph)
library(fgsea)
library(pheatmap)
library(GenomicRanges)
library(kableExtra)
library(org.Dr.eg.db)
library(pathview)
library(msigdbr)
library(rWikiPathways)
theme_set(theme_bw())
panderOptions("big.mark", ",")
panderOptions("table.split.table", Inf)
panderOptions("table.style", "rmarkdown")
if (interactive()) setwd(here::here("analysis"))Data load
dgeList <- read_rds(here::here("data","dgeList.rds"))
entrezGenes <- dgeList$genes %>%
dplyr::filter(!is.na(entrez_gene)) %>%
unnest(entrez_gene) %>%
dplyr::rename(entrez_gene = entrez_gene)
topTable <- file.path(here::here("output", "topTable.csv")) %>%
read_csv()Gene ranks
ranks <- topTable %>%
mutate(stat = -sign(logFC) * log10(PValue)) %>%
dplyr::arrange(stat) %>%
with(structure(stat, names = ensembl_gene_id))Databases used for testing
Hallmark Gene Sets
hallmark <- msigdbr("Danio rerio", category = "H") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(ensembl_gene_id)) %>%
distinct(gs_name, ensembl_gene_id, .keep_all = TRUE)
hallmarkByGene <- hallmark %>%
split(f = .$ensembl_gene_id) %>%
lapply(extract2, "gs_name")
hallmarkByID <- hallmark %>%
split(f = .$gs_name) %>%
lapply(extract2, "ensembl_gene_id")KEGG gene sets
kegg <- msigdbr("Danio rerio", category = "C2", subcategory = "CP:KEGG") %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(ensembl_gene_id)) %>%
distinct(gs_name, ensembl_gene_id, .keep_all = TRUE)
keggByGene <- kegg %>%
split(f = .$ensembl_gene_id) %>%
lapply(extract2, "gs_name")
keggByID <- kegg %>%
split(f = .$gs_name) %>%
lapply(extract2, "ensembl_gene_id")Wiki gene sets
wikidownload <- downloadPathwayArchive(organism = "Danio rerio", format = "gmt")
wiki <- gmtPathways(here::here("analysis", "wikipathways-20200210-gmt-Danio_rerio.gmt"))
wikilist <- names(wiki) %>%
lapply(function(x){
tibble(pathway = x, entrez_gene = wiki[[x]])
}) %>%
bind_rows() %>%
mutate(entrez_gene = as.numeric(entrez_gene)) %>%
left_join(entrezGenes) %>%
dplyr::filter(!is.na(ensembl_gene_id)) %>%
distinct(pathway, ensembl_gene_id, .keep_all = TRUE)
wikiByGene <- wikilist %>%
split(f = .$ensembl_gene_id) %>%
lapply(extract2, "pathway")
wikiByID <- wikilist %>%
split(f = .$pathway) %>%
lapply(extract2, "ensembl_gene_id")Gene Set Enrichment analysis (GSEA)
Hallmark pathways
set.seed(22)
# Run GSEA for hallmark
fgseaHallmark <- fgsea(hallmarkByID, ranks, nperm=1e5) %>%
as_tibble() %>%
dplyr::rename(FDR = padj) %>%
mutate(padj = p.adjust(pval, "bonferroni")) %>%
dplyr::arrange(pval)
fgseaHallmarkTop <- fgseaHallmark %>%
dplyr::filter(padj < 0.05)
fgseaHallmarkTop %>%
dplyr::select(-leadingEdge, -nMoreExtreme) %>%
pander(
style = "rmarkdown",
split.tables = Inf,
justify = "lrrrrrr",
caption = paste(
"The", nrow(.), "most significantly enriched Hallmark pathways.",
"This corresponds to an FDR of", percent(max(.$FDR)))
)| pathway | pval | FDR | ES | NES | size | padj |
|---|---|---|---|---|---|---|
| HALLMARK_OXIDATIVE_PHOSPHORYLATION | 1.22e-05 | 7.733e-05 | -0.5558 | -1.9 | 202 | 0.0006101 |
| HALLMARK_MTORC1_SIGNALING | 1.224e-05 | 7.733e-05 | -0.5328 | -1.818 | 198 | 0.0006118 |
| HALLMARK_XENOBIOTIC_METABOLISM | 1.229e-05 | 7.733e-05 | -0.524 | -1.783 | 193 | 0.0006143 |
| HALLMARK_GLYCOLYSIS | 1.231e-05 | 7.733e-05 | -0.5341 | -1.816 | 191 | 0.0006154 |
| HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION | 1.234e-05 | 7.733e-05 | -0.5348 | -1.814 | 187 | 0.0006171 |
| HALLMARK_G2M_CHECKPOINT | 1.239e-05 | 7.733e-05 | -0.7154 | -2.42 | 182 | 0.0006197 |
| HALLMARK_E2F_TARGETS | 1.247e-05 | 7.733e-05 | -0.6879 | -2.317 | 174 | 0.0006236 |
| HALLMARK_FATTY_ACID_METABOLISM | 1.264e-05 | 7.733e-05 | -0.5648 | -1.883 | 157 | 0.0006319 |
| HALLMARK_CHOLESTEROL_HOMEOSTASIS | 1.392e-05 | 7.733e-05 | -0.6557 | -1.997 | 76 | 0.0006959 |
| HALLMARK_INTERFERON_GAMMA_RESPONSE | 8.888e-05 | 0.0004444 | -0.5242 | -1.741 | 152 | 0.004444 |
| HALLMARK_ESTROGEN_RESPONSE_LATE | 0.000184 | 0.0008364 | -0.4788 | -1.631 | 195 | 0.0092 |
| HALLMARK_COAGULATION | 0.0002102 | 0.0008759 | -0.543 | -1.75 | 117 | 0.01051 |
| HALLMARK_MYC_TARGETS_V1 | 0.0002328 | 0.0008952 | -0.4753 | -1.62 | 197 | 0.01164 |
| HALLMARK_MYOGENESIS | 0.0007132 | 0.002547 | -0.4647 | -1.58 | 192 | 0.03566 |
# Make a table plot of significant Hallmark pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
hallmarkByID[dplyr::filter(fgseaHallmark, padj < 0.05)$pathway], ranks, fgseaHallmark, gseaParam = 0.5
)
KEGG pathways
# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for KEGG
fgseaKEGG <- fgsea(keggByID, ranks, nperm=1e5) %>%
as_tibble() %>%
dplyr::rename(FDR = padj) %>%
mutate(padj = p.adjust(pval, "bonferroni")) %>%
dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaKEGGTop <- fgseaKEGG %>%
dplyr::filter(padj < 0.05)
fgseaKEGGTop %>%
dplyr::select(-leadingEdge, -nMoreExtreme) %>%
pander(
style = "rmarkdown",
split.tables = Inf,
justify = "lrrrrrr",
caption = paste(
"The", nrow(.), "most significantly enriched KEGG pathways.",
"This corresponds to an FDR of", percent(max(.$FDR)))
)| pathway | pval | FDR | ES | NES | size | padj |
|---|---|---|---|---|---|---|
| KEGG_ECM_RECEPTOR_INTERACTION | 1.399e-05 | 0.0009883 | -0.6872 | -2.073 | 71 | 0.002602 |
| KEGG_FATTY_ACID_METABOLISM | 1.487e-05 | 0.0009883 | -0.7538 | -2.097 | 43 | 0.002767 |
| KEGG_BETA_ALANINE_METABOLISM | 1.594e-05 | 0.0009883 | -0.835 | -2.047 | 22 | 0.002965 |
| KEGG_GLUTATHIONE_METABOLISM | 2.985e-05 | 0.001388 | -0.7293 | -2.02 | 42 | 0.005552 |
| KEGG_CELL_CYCLE | 3.983e-05 | 0.001482 | -0.5819 | -1.861 | 109 | 0.007408 |
| KEGG_DNA_REPLICATION | 7.616e-05 | 0.002361 | -0.732 | -1.954 | 34 | 0.01417 |
| KEGG_PYRIMIDINE_METABOLISM | 0.0001228 | 0.00323 | -0.5835 | -1.816 | 89 | 0.02283 |
| KEGG_BUTANOATE_METABOLISM | 0.0001389 | 0.00323 | -0.7416 | -1.932 | 30 | 0.02584 |
| KEGG_FOCAL_ADHESION | 0.000173 | 0.003575 | -0.4842 | -1.643 | 186 | 0.03217 |
| KEGG_OXIDATIVE_PHOSPHORYLATION | 0.0001954 | 0.003634 | -0.5379 | -1.752 | 127 | 0.03634 |
pv.out <- pathview(gene.data = ranks,
pathway.id = "00010",
species = "Danio rerio",
gene.idtype = "ENSEMBL",
limit = list(gene=5, cpd=1))[1] "Note: 1192 of 19396 unique input IDs unmapped."# Make a table plot of significant KEGG pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
keggByID[fgseaKEGGTop$pathway], ranks, fgseaKEGG, gseaParam = 0.5
)
WikiPathways
# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for WikiPathways
fgseaWiki <- fgsea(wikiByID, ranks, nperm=1e5) %>%
as_tibble() %>%
dplyr::rename(FDR = padj) %>%
mutate(padj = p.adjust(pval, "bonferroni")) %>%
dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaWikiTop <- fgseaWiki %>%
dplyr::filter(padj < 0.05)
fgseaWikiTop %>%
dplyr::select(-leadingEdge, -nMoreExtreme) %>%
pander(
style = "rmarkdown",
split.tables = Inf,
justify = "lrrrrrr",
caption = paste(
"The", nrow(.), "most significantly enriched Wiki pathways.",
"This corresponds to an FDR of", percent(max(.$FDR)))
)| pathway | pval | FDR | ES | NES | size | padj |
|---|---|---|---|---|---|---|
| Cell cycle%WikiPathways_20200210%WP1393%Danio rerio | 1.401e-05 | 0.0004241 | -0.6997 | -2.109 | 71 | 0.001163 |
| G1 to S cell cycle control%WikiPathways_20200210%WP445%Danio rerio | 1.463e-05 | 0.0004241 | -0.7369 | -2.097 | 49 | 0.001214 |
| DNA Replication%WikiPathways_20200210%WP451%Danio rerio | 1.533e-05 | 0.0004241 | -0.807 | -2.117 | 31 | 0.001272 |
| Cholesterol Biosynthesis%WikiPathways_20200210%WP1387%Danio rerio | 0.0002464 | 0.005113 | -0.837 | -1.894 | 15 | 0.02045 |
# Make a table plot of significant WikiPathways pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
wikiByID[fgseaWikiTop$pathway], ranks, fgseaWiki, gseaParam = 0.5
)
Data export
GSEAresult <- bind_rows(
fgseaHallmark,
fgseaKEGG,
fgseaWiki
) %>%
dplyr::filter(padj < 0.05) %>%
dplyr::select(
pathway, ES, NES, size, padj
)
write_csv(GSEAresult,here::here("output","GSEA_resulst.csv"))
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────
setting value
version R version 3.6.0 (2019-04-26)
os macOS Mojave 10.14.6
system x86_64, darwin15.6.0
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Adelaide
date 2020-02-27
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rmarkdown 1.15 2019-08-21 [1] CRAN (R 3.6.0)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.0)
RSQLite 2.1.2 2019-07-24 [1] CRAN (R 3.6.0)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.6.0)
rvest 0.3.4 2019-05-15 [1] CRAN (R 3.6.0)
rWikiPathways * 1.4.1 2019-07-30 [1] Bioconductor
S4Vectors * 0.22.0 2019-05-02 [1] Bioconductor
scales * 1.0.0 2018-08-09 [1] CRAN (R 3.6.0)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
shiny 1.3.2 2019-04-22 [1] CRAN (R 3.6.0)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
swfdr * 1.10.0 2019-05-02 [1] Bioconductor
testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.0)
tibble * 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
tidygraph * 1.1.2 2019-02-18 [1] CRAN (R 3.6.0)
tidyr * 0.8.3 2019-03-01 [1] CRAN (R 3.6.0)
tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.0)
tidyverse * 1.2.1 2017-11-14 [1] CRAN (R 3.6.0)
tweenr 1.0.1 2018-12-14 [1] CRAN (R 3.6.0)
usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
variancePartition * 1.14.0 2019-05-02 [1] Bioconductor
vctrs 0.2.0 2019-07-05 [1] CRAN (R 3.6.0)
viridis 0.5.1 2018-03-29 [1] CRAN (R 3.6.0)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.0)
webshot 0.5.1 2018-09-28 [1] CRAN (R 3.6.0)
whisker 0.4 2019-08-28 [1] CRAN (R 3.6.0)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
workflowr 1.6.0 2019-12-19 [1] CRAN (R 3.6.0)
xfun 0.9 2019-08-21 [1] CRAN (R 3.6.0)
XML 3.98-1.20 2019-06-06 [1] CRAN (R 3.6.0)
xml2 1.2.2 2019-08-09 [1] CRAN (R 3.6.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 3.6.0)
XVector 0.24.0 2019-05-02 [1] Bioconductor
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.0)
zlibbioc 1.30.0 2019-05-02 [1] Bioconductor
[1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library