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geneSet-enrichment.R
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geneSet-enrichment.R
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# ——————————————————————————————————————————————————————
# geneSet-enrichment.R
# AUTHOR: Amal Katrib
# - - - - - - - - - - - - - - - - - -
# OBJECTIVE:
# Conduct gene-set enrichment analysis using Enrichr's
# curated list of public libraries to draw biological insights
# - - - - - - - - - - - - - - - - - -
# PREREQS:
# [ gene.txt ] genes of interest, saved in the appropriate folder
# [ similar genes ] spatially-correlated genes, saved in the appropriate folder
# [ online analysis : Enrichr.com, to analyze & download enrichment results
#
# ——————————————————————————————————————————————————————
rm( list = ls (all = TRUE))
options(stringsAsFactors = F)
# load packages
library(dplyr)
library(stringr)
library(enrichR)
# ------------------------------------------------------
# MANUAL INPUT
# ------------------------------------------------------
type = "a" # specify which dataset to analyze, selecting from: "a", "b", "c"
# set corresponding directory for data input and analysis output
dirIn = ifelse(type == "a", "dirA1/", ifelse(type == "b", "dirB1/", ifelse(type == "c", "dirC1/", NA)))
dirAnalysis = ifelse(type == "a", "dirA2/", ifelse(type == "b", "dirB2/", ifelse(type == "c", "dirC2/", NA)))
# specify analysis type, selecting from:
# "allGene": genes of interest + ALL spatially-correlated genes/proteins from separate analysis
# "intersectingGene": genes of interest + spatially-correlated genes proteins from separate analysis
analysisType = "allGene"
# set enrichr filtering parameters
p1 = 0.05 # fisher exact p-val
c1 = 10 # combined Score = log(p-value) * z.score
d = 0.25 # limit # of findings from a single database by discarding a % with lowest combined score
# ------------------------------------------------------
# DATA INPUT
# ..............
setwd(dirAnalysis)
# ------------------------------------------------------
# load gene list & adjust data input format to remove duplicates & sort
genes = read.table("genes.txt"())
genes = genes[,1] %>% unique() %>% sort
# ------------------------------------------------------
# PRELIMINARY DATA ANALYSIS
# ------------------------------------------------------
# load genes with similar spatial expression (from prior analysis))
genes.similar = list.files(path = dirAnalysis, full.names = T)
genes.similar = genes.similar[grep(analysisType, genes.similar)]
names = gsub(".*//(.*)\\_all.*", "\\1", genes.similar)
genes.similar = lapply(genes.similar, function(x) read.table(x) %>% unlist(use.names = F))
names(genes.similar) = names
# save non-empty entries
lapply(seq_along(genes.in.similar), function(i) {
write.table(genes.in.similar[[i]], file = paste0(names(genes.in.similar)[i],
"_spatiallySimilarGenes.txt"), row.names = F, col.names = F, quote = F) })
# save altogether
write.table(c(names(genes.in.similar),
unlist(genes.in.similar, use.names = F)) %>% unique,
file = "listAllSpatiallySimilarGenes.txt", row.names = F, col.names = F, quote = F)
# don't forget to include the primary genes of interest
genes.similar = lapply(seq_along(genes.similar),
function(i) c(names(genes.similar)[i], genes.similar[[i]]))
names(genes.similar) = names
# --------------------------------------------------
# GENE-SET ENRICHMENT ANALYSIS
# --------------------------------------------------
#### ONLINE: get enrichr results from gene query search online
x = list.files()[grep(".txt", list.files())]
enrichr = lapply(1:length(x), function(i) read.csv(x[i], header = T, sep = "\t"))
names(enrichr) = gsub("_table.txt", "", x)
#### OFFLINE: using enrichr libraries
# grab all libraries from EnrichR. Make sure to comment out irrelevant libraries,
# for example, those that are: specific to a disease, outdated, etc.
dbs = listEnrichrDbs() %>% arrange(desc(libraryName))
ind = c(grep("mouse", dbs$libraryName, ignore.case = T),
grep("cancer", dbs$libraryName, ignore.case = T),
grep("onco", dbs$libraryName, ignore.case = T),
grep("computational", dbs$libraryName, ignore.case = T),
grep("microbe", dbs$libraryName, ignore.case = T),
grep("achilles", dbs$libraryName, ignore.case = T),
grep("virus", dbs$libraryName, ignore.case = T),
grep("muscle", dbs$libraryName, ignore.case = T),
grep("virus", dbs$libraryName, ignore.case = T),
grep("2017b", dbs$libraryName, ignore.case = T),
grep("gtex", dbs$libraryName, ignore.case = T),
grep("crispr", dbs$libraryName, ignore.case = T),
grep("LINCS", dbs$libraryName, ignore.case = T),
grep("GWAS", dbs$libraryName, ignore.case = T),
grep("NIH_Funded_", dbs$libraryName, ignore.case = T),
grep("microRNA", dbs$libraryName, ignore.case = T),
grep("miRTarBase", dbs$libraryName, ignore.case = T),
grep("Transcription", dbs$libraryName, ignore.case = T),
grep("TF-LOF", dbs$libraryName, ignore.case = T),
grep("TF_", dbs$libraryName, ignore.case = T),
grep("Phosphatase", dbs$libraryName, ignore.case = T),
grep("Pfam", dbs$libraryName, ignore.case = T),
grep("InterPro", dbs$libraryName, ignore.case = T),
grep("Homolo", dbs$libraryName, ignore.case = T),
grep("CHEA_", dbs$libraryName, ignore.case = T),
grep("_Coexp", dbs$libraryName, ignore.case = T),
grep("BioPlex", dbs$libraryName, ignore.case = T),
grep("CORUM", dbs$libraryName, ignore.case = T),
grep("NURSA", dbs$libraryName, ignore.case = T),
grep("Allen", dbs$libraryName, ignore.case = T),
grep("Aging", dbs$libraryName, ignore.case = T),
grep("MCF7", dbs$libraryName, ignore.case = T),
grep("lncHub", dbs$libraryName, ignore.case = T),
grep("Kinase", dbs$libraryName, ignore.case = T),
grep("KEA_", dbs$libraryName, ignore.case = T),
grep("Rare_Diseases", dbs$libraryName, ignore.case = T),
grep("SubCell", dbs$libraryName, ignore.case = T),
grep("JASPAR", dbs$libraryName, ignore.case = T),
grep("Ligand", dbs$libraryName, ignore.case = T),
grep("L1000", dbs$libraryName, ignore.case = T),
grep("SILAC", dbs$libraryName, ignore.case = T),
grep("Gene_Atlas", dbs$libraryName, ignore.case = T),
grep("Browser_", dbs$libraryName, ignore.case = T),
grep("Histone", dbs$libraryName, ignore.case = T),
grep("Metabolites", dbs$libraryName, ignore.case = T),
grep("Epigenomics", dbs$libraryName, ignore.case = T),
grep("PPI_", dbs$libraryName, ignore.case = T),
grep("ESCAPE", dbs$libraryName, ignore.case = T),
grep("ENCODE", dbs$libraryName, ignore.case = T),
grep("Chromosome", dbs$libraryName, ignore.case = T),
grep("CMAP_", dbs$libraryName, ignore.case = T))
dbs_tmp = dbs
dbs_tmp$libraryName = gsub('_Human', '', dbs_tmp$libraryName)
dbs_tmp$libraryName = gsub('_[[:digit:]]+', '', dbs_tmp$libraryName)
ind = c(ind, which(duplicated(dbs_tmp$libraryName))) %>% unique()
dbs = dbs$libraryName[-ind]
rm(dbs_tmp)
# run enichR offline for genes WITH spatially-similar genes
enrichr = lapply(genes.similar, function(i) enrichr(i, dbs))
enrichr_copy = enrichr #save copy
# remove missing database entries in list
for (i in 1:length(enrichr)) {
enrichr[[i]] = Filter(function(x) nrow(x) > 0, enrichr[[i]])
enrichr[[i]] = Filter(function(x) !is.null(x), enrichr[[i]]) }
# ------------------------------------------------------
# ENRICHMENT FILTERING
# ------------------------------------------------------
# filter enrichr results to extract top enriched terms (pathways, processes, etc.),
# arrange by % genes/term; overlapping gene set size; combined score,
# and then filter to only keep top 50-75% entries from each db
top = list()
#### Enrichment filtering for all spatially-similar genes
for (i in 1:length(enrichr)) {
top[[i]] = lapply(enrichr[[i]], function(x) x %>% filter(Combined.Score >= c1, P.value <= p1))
top[[i]] = lapply(top[[i]], function(x) x %>%
select(Term, Genes, P.value, Adjusted.P.value, Combined.Score, Overlap)) %>%
do.call(rbind.data.frame, .)
top[[i]] = top[[i]] %>% mutate(db = gsub("\\.*", "", rownames(top[[i]])))
top[[i]]$db = gsub('[[:digit:]]+', '', top[[i]]$db)
top[[i]]$db = gsub("\\_$", "", top[[i]]$db)
rownames(top[[i]]) = 1:nrow(top[[i]])
top[[i]] = top[[i]] %>%
arrange(desc(as.integer(str_count(top[[i]]$Genes, ";")/as.integer(gsub(".*/", "", top[[i]]$Overlap)))),
desc(str_count(top[[i]]$Genes, ";"))) %>%
group_by(db) %>%
# filter(Combined.Score > quantile(Combined.Score, d)) %>%
arrange(db) %>%
as.data.frame()
}
names(top) = names
# add space before "Overlap" column to prevent excel converstion to date format
for (i in 1:length(top)) {
top[[i]]$Overlap = paste(" ", top[[i]]$Overlap)
}
# ------------------------------------------------------
# SAVE RESULTS
# ..............
setwd("../../functionalAnalysis/")
# ------------------------------------------------------
# create or use existing gene list folders to save enrichment results
lapply(seq_along(top), function(i) {
dir.create(file.path(names(top)[i]), showWarnings = F)
write.csv(top[[i]], file = paste0(names(top)[i], "/",
names(top)[i], "_enrichR_withSpatiallySimilarGenes.csv"), row.names = F )})
# --------------------------------------------------
# SAVE SESSION
# --------------------------------------------------
# save workspace + session info
save( list = ls(), file = paste0("SessionInfo/functionalInsights", "_", substring(Sys.Date(), 3), ".Rdata"))
writeLines(capture.output(sessionInfo()), "SessionInfo/functionalInsights_SessionInfo.txt")