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mgc_geneset_analysis_functions.R
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################################
# Name: MacIntosh Cornwell
# Email: mcornwell1957@gmail.com
################################
## Gene Set Analysis - via clusterprofiler - to be expanded upon as needed: https://yulab-smu.github.io/clusterProfiler-book/chapter1.html
## Load in Libraries
packagelist = c("clusterProfiler", "tools", "org.Hs.eg.db", "msigdbr", "GO.db", "stringdist", "enrichplot")
junk <- lapply(packagelist, function(xxx) suppressMessages(require(xxx, character.only = TRUE,quietly=TRUE,warn.conflicts = FALSE)))
## Load in data
#indeseqfile = "/Users/tosh/Desktop/Ruggles_Lab/projects/platelet-diabetes/output/deseq/deseq_results_comp_diabetes__DM_v_nonDM.csv"
#indeseqtable = read.table(indeseqfile, header = TRUE, row.names = 1, sep = ",")
# http://software.broadinstitute.org/gsea/msigdb/index.jsp
# H - hallmarks
# C1 - positional along each chromosome
# C2 - curated
# CGP - chemical and genetic perturbations
# CP - canonical
# CP:BIOCARTA - Biocarta
# CP:REACTOME - reactome
# CP:KEGG
# C3 - motif
# MIR: microRNA
# TFT: TFs and their targets
# C4 - computational - data mined cancer microarrays
# CGN - cancer gene neighborhoods
# CM - cancer modules
# C5 - GO gene sets
# BP - GO biological
# CC - GO cellular components
# MF - GO molecular function
# C6 - oncogenic gene sets
# C7 - immunological gene sets
# species for msigdbr: "Bos taurus" "Caenorhabditis elegans" "Canis lupus familiaris"
# "Danio rerio" "Drosophila melanogaster" "Gallus gallus" "Homo sapiens"
# "Mus musculus" "Rattus norvegicus" "Saccharomyces cerevisiae" "Sus scrofa"
geneset_analysis <- function(DEseqtable, rankmetric = "log2fc", pvalcutoffparam = 1,
genesetparam = c("CP:KEGG"), speciesparam = "Homo sapiens", seedparam = NULL, customgeneset = NULL) {
if (is.null(seedparam)){
seedparamin = FALSE
} else {
seedparamin = TRUE
}
if (rankmetric == "log2fc") {
gseaintable = DEseqtable[DEseqtable[,5] < pvalcutoffparam ,"log2FoldChange", drop = FALSE]
gsearanklist = sort(setNames(object = gseaintable[,1], nm = rownames(gseaintable)), decreasing = TRUE)
}
if (rankmetric == "adjpval") {
gseaintable = DEseqtable[DEseqtable[,5] < pvalcutoffparam ,c("log2FoldChange","padj"), drop = FALSE]
gseaintable$pstat <- -log10(gseaintable$padj) * ifelse(gseaintable$log2FoldChange > 0, 1, -1)
gsearanklist = sort(setNames(object = gseaintable[,"pstat"], nm = rownames(gseaintable)), decreasing = TRUE)
}
## Msigdb table generation
genesetcategories = c("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7")
genesetsubcategories = c("CGP", "CP", "CP:BIOCARTA", "CP:REACTOME", "CP:KEGG",
"MIR", "TFT", "CGN", "CM", "BP", "CC", "MF")
genesetcategories_select = genesetcategories[genesetcategories %in% genesetparam]
genesetsubcategories_select = genesetsubcategories[genesetsubcategories %in% genesetparam]
if (length(genesetcategories_select) > 0) {
m_t2g_cat <- as.data.frame(msigdbr(species = speciesparam,
category = c(genesetcategories_select))[,c("gs_name", "gene_symbol")])
} else {m_t2g_cat = NULL}
if (length(genesetsubcategories_select) > 0) {
m_t2g_subcat <- as.data.frame(msigdbr(species = speciesparam,
subcategory = c(genesetsubcategories_select))[,c("gs_name", "gene_symbol")])
} else {m_t2g_subcat = NULL}
m_t2g = rbind(m_t2g_cat, m_t2g_subcat)
## If you want to test a single custom geneset - input here to override the null argument, and will remake the m_t2g object
## Custom object needs to be 2 columns, with the genesets in the first col, and the genes in the second col
# gs_name gene_symbol
# 1 GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS ALDH1L1
# 2 GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS ALDH1L2
if (!is.null(customgeneset)) {
colnames(customgeneset) <- c("gs_name", "gene_symbol")
m_t2g <- rbind(customgeneset, m_t2g)
}
gseaout = GSEA(geneList = gsearanklist, TERM2GENE = m_t2g, by = "fgsea",
minGSSize = 1, maxGSSize = 1000,
# nPerm = 10000, ## Apparently they dont want us to use nPerm anymore, so removing this! 2021-08-18
seed = seedparamin,
pvalueCutoff = 1.1, verbose = TRUE)
return(gseaout)
}
hypergeo_genetest <- function(DEseqtable, statcutoffparam = c("stattype" = "pvalue", "pstatcutoff" = 0.01, "log2fccutoff" = 0),
genesetparam = c("C5"), speciesparam = "Homo sapiens",
customgeneset = NULL) {
## This allows you to just input a list of genes and override the input for a DEseq table
if (length(DEseqtable) == 1){
geotestUPlist <- geotestDOWNlist <- DEseqtable[,1]
} else {
geotestUPlist = rownames(DEseqtable[
DEseqtable[,statcutoffparam["stattype"]] < as.numeric(statcutoffparam["pstatcutoff"]) &
!is.na(DEseqtable[,statcutoffparam["stattype"]]) &
DEseqtable[,"log2FoldChange"] > as.numeric(statcutoffparam["log2fccutoff"]),, drop = FALSE])
geotestDOWNlist = rownames(DEseqtable[
DEseqtable[,statcutoffparam["stattype"]] < as.numeric(statcutoffparam["pstatcutoff"]) &
!is.na(DEseqtable[,statcutoffparam["stattype"]]) &
DEseqtable[,"log2FoldChange"] < -as.numeric(statcutoffparam["log2fccutoff"]),, drop = FALSE])
}
## Msigdb table generation
genesetcategories = c("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7")
genesetsubcategories = c("CGP", "CP", "CP:BIOCARTA", "CP:REACTOME", "CP:KEGG",
"MIR", "TFT", "CGN", "CM", "BP", "CC", "MF")
genesetcategories_select = genesetcategories[genesetcategories %in% genesetparam]
genesetsubcategories_select = genesetsubcategories[genesetsubcategories %in% genesetparam]
if (length(genesetcategories_select) > 0) {
m_t2g_cat <- as.data.frame(msigdbr(species = speciesparam,
category = c(genesetcategories_select))[,c("gs_name", "gene_symbol")])
} else {m_t2g_cat = NULL}
if (length(genesetsubcategories_select) > 0) {
m_t2g_subcat <- as.data.frame(msigdbr(species = speciesparam,
subcategory = c(genesetsubcategories_select))[,c("gs_name", "gene_symbol")])
} else {m_t2g_subcat = NULL}
m_t2g = rbind(m_t2g_cat, m_t2g_subcat)
## If you want to test a single custom geneset - input here to override the null argument, and will remake the m_t2g object
## Custom object needs to be 2 columns, with the genesets in the first col, and the genes in the second col
# gs_name gene_symbol
# 1 GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS ALDH1L1
# 2 GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS ALDH1L2
if (!is.null(customgeneset)) {
colnames(customgeneset) <- c("gs_name", "gene_symbol")
m_t2g <- rbind(customgeneset, m_t2g)
}
enricherUPout = enricher(gene = geotestUPlist, qvalueCutoff = 2, pvalueCutoff = 2,
minGSSize = 0, maxGSSize = 10000, TERM2GENE = m_t2g)
enricherDOWNout = enricher(gene = geotestDOWNlist, qvalueCutoff = 2, pvalueCutoff = 2,
minGSSize = 0, maxGSSize = 10000, TERM2GENE = m_t2g)
if (is.null(enricherUPout) & is.null(enricherDOWNout)) {
enricherUPout <- enricherDOWNout <- data.frame(matrix(nrow = 0, ncol = 9,
dimnames = list(NULL, c("ID", "Description", "GeneRatio", "BgRatio", "pvalue", "p.adjust", "qvalue", "geneID", "Count"))))
}
return(list(enricherUPout = enricherUPout, enricherDOWNout = enricherDOWNout))
}
gsea_barplot <- function(gseaout, pstatparam, numterms = 10, titleparam, clean_genesetIDs = FALSE){
if (pstatparam == "pvalue"){pstat = 6}
if (pstatparam == "p.adjust"){pstat = 7}
gseaplotin = data.frame(gseaout)[order(abs(gseaout[,pstatparam]), decreasing = FALSE)[1:numterms],c(1,5,pstat)]
gseaplotin = gseaplotin[order(gseaplotin[,"NES"], decreasing = TRUE),]
if (clean_genesetIDs == TRUE) {
gseaplotin[,1] <- unlist(lapply(gsub("_", " ", gsub("GOBP_|GOMF_|GOCC_", "", gseaplotin[,1])),
function(x) simpleCap(tolower(x))))
} else {
gseaplotin[,1] = gsub("_", " ", gseaplotin[,1])
}
#pout <- ggplot(gseaplotin, mapping = aes(x = gseaplotin[,1], y = gseaplotin[,2], fill = gseaplotin[,3]))
pout <- ggplot(gseaplotin, mapping = aes(x = str_wrap(gseaplotin[,1], 20), y = gseaplotin[,2], fill = gseaplotin[,3]))
pout <- pout + geom_bar(stat = "identity")
# pout <- pout + scale_fill_gradient(low = "palegreen", high = "darkgreen", limits=c(0,0.1))
pout <- pout + scale_fill_viridis(limits = c(0,max(0.1, max(gseaplotin[,3]))), direction = -1, option = "magma")
pout <- pout + scale_x_discrete(limits=rev(str_wrap(gseaplotin[,1], 20)))
pout <- pout + coord_flip()
pout <- pout + labs(x="Geneset", y = "NES", fill = pstatparam, title = titleparam)
pout
return(pout)
}
## Takes output from gsea_analysis function above ^^
gsea_custom_randomwalkplot <- function(gseaobject, geneSetID, addNESvalue = TRUE) {
gseaplot_out <- gseaplot(gseaobject, geneSetID = geneSetID, title = geneSetID)
if (addNESvalue) {
# Place text very specifically
if (sign(gseaobject[geneSetID,"enrichmentScore"]) < 0) {
xcor <- 0.16
ycor <- 0.16
} else {
xcor <- 0.84
ycor <- 0.32
}
gseaplot_out <- gseaplot_out + annotate("text", x = xcor, y = ycor,
label = paste0("NES: ", round(gseaobject[geneSetID,"NES"], 3), "\n",
"p.adjust: ", round(gseaobject[geneSetID,"p.adjust"],4)
))
}
return(gseaplot_out)
}
## Takes output from gsea_analysis function above ^^
gsea_custom_randomwalkplot_v2 <- function(gseaobject, geneSetID, addNESvalue = TRUE) {
gseaplot_out <- gseaplot2(x = gseaobject, geneSetID = geneSetID, title = geneSetID, base_size = 16, )
if (addNESvalue) {
# Place text very specifically
if (sign(gseaobject[geneSetID,"enrichmentScore"]) < 0) {
xcor <- 0.16
ycor <- 0.16
} else {
xcor <- 0.84
ycor <- 0.32
}
gseaplot_out <- gseaplot_out + annotate("text", x = xcor, y = ycor,
label = paste0("NES: ", round(gseaobject[geneSetID,"NES"], 3), "\n",
"p.adjust: ", round(gseaobject[geneSetID,"p.adjust"],4)
))
gseaplot_out + geom_vline(xintercept = 0.9, linetype = 2, color = "red")
}
return(gseaplot_out)
}
gene_in_geneset_heatmap <- function(plotintable, gene_delineator = "/", clean_genesetIDs = FALSE, clusterparam = FALSE) {
melted_ID_v_gene_table <- do.call(rbind, apply(plotintable, 1, function(x) {
genes <- unlist(strsplit(x[2], split = "/"))
cbind(ID = x[1], genes)
}))
ID_v_gene_matrix <- dcast(data.frame(melted_ID_v_gene_table), ID ~ genes, fun.aggregate = length)
rownames(ID_v_gene_matrix) <- ID_v_gene_matrix[,"ID"]
if (clean_genesetIDs == TRUE) {
rownames(ID_v_gene_matrix) <- unlist(lapply(gsub("_", " ", gsub("GOBP_|GOMF_|GOCC_", "", ID_v_gene_matrix[,"ID"])),
function(x) simpleCap(tolower(x))))
}
create_heatmap(counttab = ID_v_gene_matrix[,!grepl("ID", colnames(ID_v_gene_matrix))], subsetnum = FALSE, scale_data = FALSE,
colclusterparam = clusterparam, rowclusterparam = clusterparam,
heatmapcolorparam = colorRamp2(breaks = c(0,1), colors = c("white", "black")), addborders = TRUE)
}
## Convert goterms to go ids
goterm_to_goID <- function(ingoterms) {
## First read in our GO.db list of all go terms
allgoterms <- Term(GOTERM)
cleaned_allgoterms <- tolower(gsub("-|\\/", " ", allgoterms))
## Then clean our inputted list of goterms
cleaned_ingoterms <- gsub("_", " ", tolower(gsub("GO_", "", ingoterms)))
## Then find which we have, which we done, and return all
# Separated for clarity and failsafing
p1 <- data.frame(cleaned_GOterm = cleaned_allgoterms[cleaned_allgoterms %in% cleaned_ingoterms],
GOID = names(cleaned_allgoterms[cleaned_allgoterms %in% cleaned_ingoterms]), stringsAsFactors = FALSE)
p2 <- data.frame(cleaned_GOterm = cleaned_ingoterms,
Input_GOterm = ingoterms)
foundIDs <- merge(p1, p2, all.x = TRUE, by = "cleaned_GOterm", sort = FALSE)
## For the not found - lets find a closest match
notfoundIDs <- cleaned_ingoterms[!cleaned_ingoterms %in% cleaned_allgoterms]
## Can we return a closest go term for each one of these? YES
ClosestMatch2 = function(string, stringVector){stringVector[amatch(string, stringVector, maxDist=Inf)]}
closematchtab1 <- data.frame(sapply(notfoundIDs, function(x) ClosestMatch2(x, cleaned_allgoterms)))
closematchtab1[,c(2,3)] <- do.call(rbind, strsplit(rownames(closematchtab1), split = "\\."))
colnames(closematchtab1) <- c("ClosestMatch_GOterm", "cleaned_GOterm", "GOID")
closematchtab1 <- closematchtab1[,c("GOID", "ClosestMatch_GOterm", "cleaned_GOterm")]
rownames(closematchtab1) <- NULL
# Reattach the original input term
closematchtab2 <- cbind(closematchtab1, Input_GOterm = ingoterms[!cleaned_ingoterms %in% cleaned_allgoterms])
return(list(converted_goterm_to_goID = foundIDs, missing_goterm_to_id = closematchtab2))
}
## Import a vecotr of goIDS - return a table of the ancestors
find_goterm_ancestors <- function(ingoids) {
## Define the function we need
getAllBPAncestors <- function(goids, GOBPANCESTOR_inlist = NULL){
if (is.null(GOBPANCESTOR_inlist)) {GOBPANCESTOR_inlist = as.list(GOBPANCESTOR)}
## Apparently not all goids HAVE ancestors. So.... we need an if statement to only run this if they have an ancestor, if not, return itself?
if (goids %in% names(GOBPANCESTOR_inlist)) {
ans <- unique(unlist(mget(goids, GOBPANCESTOR), use.names=FALSE))
ans <- c(ans[!is.na(ans)], goids)
} else {
ans <- goids
}
return(ans)
}
## Then apply over our ingoid vector this function to find our ancestors
# Need to predefine out BPAncestors object because thats the major time crunch to do each time
GOBPANCESTOR_inlist = as.list(GOBPANCESTOR)
anc_out1 <- lapply(ingoids, function(x) getAllBPAncestors(x, GOBPANCESTOR_inlist))
ancestor_count <- table(unlist(anc_out1))
ancestor_count <- data.frame(ancestor_count[order(ancestor_count, decreasing = TRUE)])
rownames(ancestor_count) <- ancestor_count[,1]
# with the ancestor count table - see what GO terms we have and attach as annotation
allgoterms <- data.frame(Term(GOTERM), stringsAsFactors = FALSE)
ancestor_counttab_annot <- merge(ancestor_count, allgoterms, by = "row.names", all.x = TRUE, sort = FALSE)
outtab <- ancestor_counttab_annot[,c(2:4)]
colnames(outtab) <- c("GOID", "Number_of_Mentions", "GOTerm")
# Return this table
return(ancestral_GOterm_counttable = outtab, ancesterlist = anc_out1)
}
# getAllBPChildren <- function(goids)
# {
# ans <- unique(unlist(mget(goids, GOBPCHILDREN), use.names=FALSE))
# ans <- ans[!is.na(ans)]
# }
# getAllBPAncestors <- function(goids)
# {
# ans <- unique(unlist(mget(goids, GOBPANCESTOR), use.names=FALSE))
# ans <- ans[!is.na(ans)]
# }
# ## Classic Random Walk Scoring table for GSEA Plot
# library(DOSE)
# data(geneList)
# de <- names(geneList)[abs(geneList) > 2]
# edo2 <- gseDO(geneList)
# p1 <- gseaplot(edo2, geneSetID = 1, by = "runningScore", title = edo2$Description[1])
# p2 <- gseaplot(edo2, geneSetID = 1, by = "preranked", title = edo2$Description[1])
# p3 <- gseaplot(edo2, geneSetID = 1, title = edo2$Description[1])
# http://software.broadinstitute.org/gsea/msigdb/index.jsp
# H - hallmarks
# C1 - positional along each chromosome
# C2 - curated
# CGP - chemical and genetic perturbations
# CP - canonical
# CP:BIOCARTA - Biocarta
# CP:REACTOME - reactome
# CP:KEGG
# C3 - motif
# MIR: microRNA
# TFT: TFs and their targets
# C4 - computational - data mined cancer microarrays
# CGN - cancer gene neighborhoods
# CM - cancer modules
# C5 - GO gene sets
# BP - GO biological
# CC - GO cellular components
# MF - GO molecular function
# C6 - oncogenic gene sets
# C7 - immunological gene sets
# species for msigdbr: "Bos taurus" "Caenorhabditis elegans" "Canis lupus familiaris"
# "Danio rerio" "Drosophila melanogaster" "Gallus gallus" "Homo sapiens"
# "Mus musculus" "Rattus norvegicus" "Saccharomyces cerevisiae" "Sus scrofa"
# geneset_heatmap <- function(DEseqtable, pvalcutoffparam = 1, genesetparam = c("CP:KEGG"), speciesparam = "Homo sapiens",
# pathwayparam = "KEGG_PENTOSE_PHOSPHATE_PATHWAY") {
#
# gseaintable = DEseqtable[DEseqtable[,5] < pvalcutoffparam ,c(2), drop = FALSE]
# gsearanklist = sort(setNames(object = gseaintable[,1], nm = rownames(gseaintable)), decreasing = TRUE)
#
# ## Msigdb table generation
# genesetcategories = c("H", "C1", "C2", "C3", "C4", "C5", "C6", "C7")
# genesetsubcategories = c("CGP", "CP", "CP:BIOCARTA", "CP:REACTOME", "CP:KEGG",
# "MIR", "TFT", "CGN", "CM", "BP", "CC", "MF")
# genesetcategories_select = genesetcategories[genesetcategories %in% genesetparam]
# genesetsubcategories_select = genesetsubcategories[genesetsubcategories %in% genesetparam]
#
# if (length(genesetcategories_select) > 0) {
# m_t2g_cat <- as.data.frame(msigdbr(species = speciesparam,
# category = c(genesetcategories_select))[,c("gs_name", "gene_symbol")])
# } else {m_t2g_cat = NULL}
# if (length(genesetsubcategories_select) > 0) {
# m_t2g_subcat <- as.data.frame(msigdbr(species = speciesparam,
# subcategory = c(genesetsubcategories_select))[,c("gs_name", "gene_symbol")])
# } else {m_t2g_subcat = NULL}
# m_t2g = rbind(m_t2g_cat, m_t2g_subcat)
#
#
# as.data.frame(msigdbr(species = "Mus musculus", subcategory = c("CP:KEGG"))[,c("gs_name", "gene_symbol")])
#
# }
# dotplot(edo, showCategory=10)
#
#
# cnetplot(gseaout, showCategory = c("KEGG_CELL_ADHESION_MOLECULES_CAMS",
# "KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS",
# "KEGG_O_GLYCAN_BIOSYNTHESIS"), foldChange = gsearanklist, categorySize="pvalue")
### GOI HEATMAP FOR A SINGLE GENE SET
# dbtab = as.data.frame(msigdbr(species = "Homo sapiens", subcategory = c("CP:KEGG")))[,c("gs_name", "gene_symbol")]
# genesetOIcounttab = data.frame(KEGG_CELL_ADHESION_MOLECULES_CAMS =
# gsearanklist[dbtab[dbtab[,1] == "KEGG_CELL_ADHESION_MOLECULES_CAMS",2]],
# row.names = dbtab[dbtab[,1] == "KEGG_CELL_ADHESION_MOLECULES_CAMS",2])
# genesetOIcounttab = genesetOIcounttab[order(genesetOIcounttab[,1], decreasing = TRUE),,drop=FALSE]
# temppdfoutfile = "/Users/tosh/Desktop/Ruggles_Lab/projects/platelet-cholesterol-human/output/test.pdf"
# genesetOI = create_heatmap(counttab = genesetOIcounttab, pdfoutfile = temppdfoutfile)
#
# heatplot(geneset_analysis_out_HALL, foldChange = gsearanklist)
#
# emapplot(geneset_analysis_out_HALL, showCategory = 10)
#
# gseaplot(geneset_analysis_out_HALL, geneSetID = "HALLMARK_COAGULATION", by="runningScore")
# gseaplot2(geneset_analysis_out_HALL, geneSetID = "HALLMARK_COAGULATION")
#
#
#
# library("pathview")
# pathview(gene.data = gsearanklist,
# pathway.id = "hsa04110",
# species = "hsa",
# limit = list(gene=max(abs(geneList)), cpd=1))
# filename=system.file("extdata/gse16873.demo", package = "pathview")
# gse16873=read.delim(filename, row.names=1)
# gse16873.d=gse16873[,2*(1:6)]-gse16873[,2*(1:6)-1]
#
# i <- 1
# pv.out <- pathview(gene.data = gse16873.d[, 1], pathway.id = demo.paths$sel.paths[i],
# species = "hsa", out.suffix = "gse16873", kegg.native = T)
# list.files(pattern="hsa04110", full.names=T)
#
######### POSSIBLE PLOTTING FUNCTIONS
# library(DOSE)
# data(geneList)
# de <- names(geneList)[abs(geneList) > 2]
#
# edo <- enrichDGN(de)
# library(enrichplot)
# barplot(edo, showCategory=20)
# barplot(geneset_analysis_out_GO, showCategory=5)
# barplot(gseaout)
# dotplot(gseaout, color = "pvalue", showCategory = 10, x = "NES", size = "setSize")
# heatplot(gseaout, foldChange = gsearanklist)
#
# barplot(edo)
### THIS ALLOWS FOR (NOT) - downloaded in 2018 LIVE CURATION OF MSIGDB GENESETS
#library(msigdbr)
# msigdbsets <- as.data.frame(msigdbr(species = "Homo sapiens"))
# #t1 = msigdbsets[msigdbsets[,3] == "C2",]
# #kegg_genesets = msigdbsets[msigdbsets[,4] == "CP:KEGG",]
#
# m_t2g <- msigdbr(species = "Homo sapiens", subcategory = "CP:KEGG") %>%
# dplyr::select(gs_name, gene_symbol)
#### WE NEED A NAMED DECREASING VECTOR - WITH THE VALUES AS LOG2FC AND NAMES AS GENES (according to vignette)
#### NOTE THAT YOU CAN DEFINE GENES AS DE FIRST THOUGH (vignette usees logfc of 2)
## Create our gene list
#gseagenelist = names(gsearanklist)
# m_t2g <- msigdbr(species = "Homo sapiens", subcategory = "CP:KEGG") %>%
# dplyr::select(gs_name, gene_symbol)
#
#
#
#
#
# data(geneList, package="DOSE")
# gene <- names(geneList)[abs(geneList) > 2]
#
# gmtfile <- system.file("extdata", "c5.cc.v5.0.entrez.gmt", package="clusterProfiler")
# c5 <- read.gmt(gmtfile)
#
# egmt <- enricher(gene, TERM2GENE=c5)
# egmt2 <- GSEA(geneList, TERM2GENE=c5, verbose=FALSE)
## ARCHIVE
#####
# genesetfile1 = "/Users/tosh/Desktop/Ruggles_Lab/databases/msigdb_20190711/c2.cp.kegg.v6.2.symbols.gmt"
# intablefile = "/Users/tosh/Desktop/Ruggles_Lab/projects/newman-pace/data/count_normalized_formatted_filt005.txt"
# indeseqfile = "/Users/tosh/Desktop/Ruggles_Lab/projects/newman-pace/data/dge.PACE-vs-Healthy.csv"
# ingenelistfile = "/Users/tosh/Desktop/Ruggles_Lab/projects/newman-pace/data/dge.PACE-vs-Healthy_GOI_qval01.txt"
#
# intable = read.table(intablefile, header = TRUE, row.names = 1, sep = ifelse(file_ext(intablefile)=="txt", "\t", ","), stringsAsFactors = FALSE, check.names = FALSE)
#
# indeseq = read.table(indeseqfile, header = TRUE, row.names = 1, sep = ifelse(file_ext(indeseqfile)=="txt", "\t", ","), stringsAsFactors = FALSE, check.names = FALSE)
# deseqGOI = sort(setNames(indeseq$log2FoldChange, as.character(row.names(indeseq))), decreasing=TRUE)
#
# ingenelist = read.table(ingenelistfile, header = FALSE, sep = ifelse(file_ext(ingenelistfile)=="txt", "\t", ","), stringsAsFactors = FALSE, check.names = FALSE)
# genelistGOI = ingenelist[,1]
#
# ## Read in GMT file
# gs1 = read.gmt(genesetfile1)
#
#
# wpgmtfile <- system.file("extdata/wikipathways-20180810-gmt-Homo_sapiens.gmt", package="clusterProfiler")
# wp2gene <- read.gmt(wpgmtfile)
# wp2gene <- wp2gene %>% tidyr::separate(ont, c("name","version","wpid","org"), "%")
# wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
# wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
# ## Looks like TERM2GENE - is just 2 columns, melted table with the first column being genesets (terms) and the second column being genes in the geneset
# ## TERM2NAME - is the conversion of "term" to the "name" - where the name is the geneset(redunant if youre geneset is already named)
#
# test1 = enricher(gene = genelistGOI, TERM2GENE = gs1, qvalueCutoff = 1, pvalueCutoff = 1)
# test2 = GSEA(geneList = deseqGOI, TERM2GENE = gs1, pvalueCutoff = 1, verbose = TRUE)
#
# #ewp <- setReadable(ewp, org.Hs.eg.db, keyType = "ENTREZID") ## Code snipper to convert entrez to genesymbol
#
# ### CELL MARKERS
# cell_markers <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Human_cell_markers.txt') %>%
# tidyr::unite("cellMarker", tissueType, cancerType, cellName, sep=", ") %>%
# dplyr::select(cellMarker, geneID) %>%
# dplyr::mutate(geneID = strsplit(geneID, ', '))
#
# test1 = enricher(gene = genelistGOI, TERM2GENE = cell_markers, qvalueCutoff = 1, pvalueCutoff = 1)