cp_enrichment.R

#!/usr/bin/env Rscript
#+ echo=FALSE, error=F


suppressMessages(require(docopt))

## todo use textual input here for ease of use
doc <- '
Perform an enrichment analysis for a set of genes
Usage: cp_enrichment.R [options] <gene_lists_tsv> <group_col>

Options:
--overlay_expr_data Tsv with overlay data for the kegg pathways
--list_id_col Column containing the grouping variable to speparate different lists [default: ]
--project <project_prefix>   Name to prefix all generated result files [default: ]
'

opts <- docopt(doc, commandArgs(TRUE)) ## does not work when spining
# opts <- docopt(doc, "--overlay_expr_data ../plot_score_matrix.txt ../degs_by_contrast.txt contrast" )

devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.20/R/core_commons.R")
devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.20/R/ggplot_commons.R")
devtools::source_url("https://raw.githubusercontent.com/holgerbrandl/datautils/v1.20/R/bio/diffex_commons.R")

require.auto(knitr)
require.auto(DT)



## to fix child support issue with knitr, see also
## http://stackoverflow.com/questions/20030523/knitr-nested-child-documents
## https://github.com/yihui/knitr/issues/38
# todo disabled because root.dir in parent document seems the only working solution
#if(exists('project_dir')) setwd(project_dir)
#print(getwd())

## load the data
geneLists <- read.delim(opts$gene_lists_tsv)
group_col=opts$group_col
geneLists %<>% group_by_(.dots=group_col)


#geneLists %<>% filter(cluster %in% c("cluster_1", "cluster_2"))

if(!is.null(opts$overlay_expr_data)){
    overlayData <- read.delim(opts$overlay_expr_data)
}

## TODO expose options to run gesa instead (by assuming that gene lists are sorted)
## see http://www.bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.pdf for details

resultsBaseName <- if(str_length(opts$project)>0) paste0(opts$project, ".") else basename(opts$gene_lists_tsv) %>% trim_ext("txt") #%>% paste0(".")
#resultsBaseName=basename(opts$gene_lists_tsv) %>% trim_ext("txt") #%>% paste0(".")

########################################################################################################################
#' ## Enrichment Analysis

unloadNamespace('dplyr'); require(dplyr)


#' This analysis was performed using [clusterProfiler](http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html). The following ontologies were tested: Kegg, Go, Reactome, Dose,

listLabels <- geneLists %>% select(-ensembl_gene_id) %>% distinct
listLabels %<>% transform(list_label=do.call(paste, c(listLabels, sep="__")))

geneLists %>% inner_join(listLabels) %>%
    ggplot(aes(list_label)) +
    geom_bar() +
    coord_flip() +
    ggtitle("gene list sizes to be tested for term enrichment") +
    ylab("")


guess_cp_species <- function(ensIds){
    an_id <-ensIds[1]

    if(str_detect(an_id, "ENSG")){
        return("human")
    }else if(str_detect(an_id, "ENSMUSG")){
        return("mouse")
    }else if(str_detect(an_id, "ENSDARG")){
        return("zebrafish")
    }else if(str_detect(an_id, "FBgn")){
        return("fly")
    }else{
        stop(paste("could not clusterProfiler species name from ", an_id))
    }
}

guess_anno_db <- function(ensIds){
    an_id <-ensIds[1]

    if(str_detect(an_id, "ENSG")){
        return("org.Hs.eg.db")
    }else if(str_detect(an_id, "ENSMUSG")){
        return("org.Mm.eg.db")
    }else if(str_detect(an_id, "ENSDARG")){
        return("org.Dr.eg.db")
    }else if(str_detect(an_id, "FBgn")){
        return("org.Dm.eg.db")
    }else{
        stop(paste("could not anno db mart from ", an_id))
    }
}

#source("http://bioconductor.org/biocLite.R")
#biocLite("org.Mm.eg.db")
#biocLite("org.Hm.eg.db")
#biocLite("org.Dr.eg.db")
#biocLite("org.Dm.eg.db")
#biocLite("KEGG.db")


#data(gcSample)
#yy = enrichKEGG(gcSample[[5]], pvalueCutoff=0.01)
#head(summary(yy))
#plot(yy)
## seems broken

cpSpecies <- guess_cp_species(geneLists$ensembl_gene_id)
annoDb <- guess_anno_db(geneLists$ensembl_gene_id) # e.g. "org.Hs.eg.db"

## supported ids
#idType("org.Hs.eg.db")

## convert to entrez gene ids

glMappedRaw <-  clusterProfiler::bitr(geneLists$ensembl_gene_id, fromType="ENSEMBL", toType="ENTREZID", annoDb=annoDb) %>%
    set_names("ensembl_gene_id", "entrez_gene_id") %>% right_join(geneLists)


#' Check how many failed to map
count(glMappedRaw, is.na(entrez_gene_id))

glMapped <- glMappedRaw %>% filter(!is.na(entrez_gene_id)) %>% select(-ensembl_gene_id)  %>%
    ## regroup the data
    #    group_by_(cluster)
    group_by_(.dots=group_col %>% ac)

## TODO  expose as argument

pCutoff <- 0.05


## retain just 1500 genes at max per group
#glMappedSub <- glMapped %>% do({shuffle(.) %>% head(1500)})
#count(glMappedSub, cluster)

## sync reactome pacakge to node
if(Sys.getenv("LSF_SERVERDIR")!="" && dir.exists("/tmp/local_r_packages")){
    system("if [ ! -d '/tmp/local_r_packages/reactome.db/' ]; then scp -r falcon:/tmp/local_r_packages /tmp ; fi")
}

require_auto(clusterProfiler)
require_auto(ReactomePA)

cp_test <- function(geneIds){
    # DEBUG geneIds <- glMapped %>% filter(cluster %in% c("cluster_3")) %$% entrez_gene_id %>% as.integer
    # DEBUG geneIds <- head(glMapped,30)$entrez_gene_id
#    geneIds=.$entrez_gene_id

    if(length(geneIds)>1500){
        geneIds <- sample(geneIds) %>% head(1500)
    }

    echo("testing", length(geneIds), " genes for enrichment")

#    PANTHER10_ontology <- read.delim("http://data.pantherdb.org/PANTHER10.0/ontology/Protein_Class_7.0")
    
#    pantherResults <-     enricher(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE, TERM2GENE = PANTHER10_ontology) %>% summary()
    keggResults <-        enrichKEGG(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE, use_internal_data=T) %>% summary()
    reactomeResults <- enrichPathway(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE) %>% summary()
    goResultsCC <-          enrichGO(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE, ont = "CC") %>% summary()
    goResultsMF <-          enrichGO(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE, ont = "MF") %>% summary()
    goResultsBP <-          enrichGO(gene = geneIds, organism = cpSpecies, pvalueCutoff = pCutoff, readable = TRUE, ont = "BP") %>% summary()

    enrResults <- rbind_list(
        mutate(keggResults, ontology="kegg"),
        mutate(reactomeResults, ontology="reactome"),
        mutate(goResultsBP, ontology="go_bp"),
        mutate(goResultsMF, ontology="go_mf"),
        mutate(goResultsCC, ontology="go_cc")
    )
}

## prety slow
enrResults <-  quote(glMapped %>% do(cp_test(.$entrez_gene_id))) %>% cache_it(paste0("enrdata_", digest(glMapped)))

## run the actual enrichment test for all clusters and all ontologies
#library(foreach); library(doMC); registerDoMC(cores=20)
## https://support.bioconductor.org/p/38541/
## library(GOstats) inside your %dopar% loop. And then start with a fresh session, so GOstats is not loaded outside the loop. It worked for me.
#enrResults <- ddply(glMapped, groups(glMapped) %>% ac, function(curGroup){
#  cp_test(curGroup$entrez_gene_id)
#}, .progress="text", .parallel=F) %>% cache_it(paste0("enrdata_", digest(glMapped)))


unloadNamespace('dplyr'); require(dplyr) ## tbd seriously???
#enrResults %<>% rename(Category=Description)
#enrResults %<>% rename(ontology=Category)


write.delim(enrResults, file=paste0(resultsBaseName, "enrResults.txt"))
# enrResults <- read.delim(paste0(resultsBaseName, "enrResults.txt"))
#'  [Enrichment Results](`r paste0(resultsBaseName, "enrResults.txt")`)

#enrResults %>% ggplot(aes(pvalue)) + geom_histogram() + scale_x_log10()
#enrResults %>% ggplot(aes(ontology)) + facet_wrap(~cluster) + geom_bar() + rot_x_lab()
#facetSpecs <- paste("~", groups(geneLists) %>% ac %>% paste(collapse=" + "))
facetSpecs <- paste("~", group_col %>% ac %>% paste(collapse=" + "))

#http://stackoverflow.com/questions/11028353/passing-string-variable-facet-wrap-in-ggplot-using-r
enrResults %>% ggplot(aes(ontology)) + facet_wrap(as.formula(facetSpecs)) + geom_bar() + rot_x_lab() + ggtitle("enriched term counts by cluster")


#' Keep at max 100 terms for visualzation per group
#if(table(enrResults$cluster))
## restablish the grouping and limit results per group to 100
#enrResults %<>% group_by_(.dots=groups(geneLists) %>% ac)
#enrResults %<>% group_by_(.dots=group_col)
enrResults %<>% sample_frac(1) %>% filter((row_number()<100)) %>% group_by_(.dots=group_col)




#+ include=FALSE, eval=FALSE
## plot the enrichment results
#sigEnrResults %>% group_by(Category, add=T) %>% do({
#    logPlot <- . %>% ggplot(aes(Term, PValue)) +
#	    geom_bar(stat="identity")+coord_flip() +
#	    xlab("Enriched Terms") +
#	    ggtitle(.$Category[1]) +
#	    scale_y_log10()
#	    print(logPlot)
#})

#sigEnrResults %>%
##    select(-Genes) %>%
#    do({
#    enrResultsGrp <- .
#    ## DEBUG enrResultsGrp <- sigEnrResults
##    geneLists %>% first(1) %>% select(-ensembl_gene_id) %>% paste0(., collapse="_")
##browser()
#    label <- geneLists %>% semi_join(enrResultsGrp) %>% first(1) %>% dplyr::select(-ensembl_gene_id) %>% paste0(., collapse="_")
#
#    echo("processing", label)
#
#    logPlot <- enrResultsGrp %>%
#        ## fix factor order
#        mutate(Term=reorder(Term, -PValue) %>% reorder(as.integer(Category))) %>%
#        ggplot(aes(Term, PValue, fill=Category)) +
#	    geom_bar(stat="identity")+
#	    coord_flip() +
#	    xlab("Enriched Terms") +
#	    ggtitle(label) +
#	    scale_y_log10()
#
#	    ggsave(paste0(resultsBaseName, label, ".enrichmed_terms.pdf"))
#	    print(logPlot)
#})
##ggsave2()

## include=FALSE, error=TRUE

#+ error=TRUE, echo=FALSE
warning("dropping levels")
enrResults %<>% mutate(ontology=ac(ontology)) ## drop unsused level to get consistent color palette

enrResults %<>%rename(Term=Description)
term_category_colors <- create_palette(unique(ac(enrResults$ontology)))

figDir <- "enr_charts"
dir.create(figDir)

term_barplot_files <- enrResults %>%
    ## chop and pad category names
    mutate(Term=str_sub(Term, 1, 70) %>% str_pad(70)) %>%
do({
    enrResultsGrp <- .
#    browser()
    ## DEBUG enrResultsGrp <- sigEnrResults
#    label <- geneLists %>% semi_join(enrResultsGrp) %>% first(1) %>% dplyr::select(-ensembl_gene_id) %>% paste0(., collapse="_")
    label <- subset(enrResultsGrp, select=group_col)[1,1] %>% as.matrix %>% ac


#browser()
    warning(paste0("processing terms", paste(ac(unique(enrResultsGrp$Category)), collapse=",")))
    logPlot <- enrResultsGrp %>%
        ## fix factor order
        mutate(Term=reorder(Term, -qvalue) %>% reorder(as.integer(as.factor(ontology)))) %>%
        ggplot(aes(Term, qvalue, fill=ontology)) +
        geom_bar(stat="identity")+
        scale_fill_manual(values = term_category_colors, drop=F, name="Ontology") +
        coord_flip() +
        xlab("Enriched Terms") +
        ggtitle(label) +
        scale_y_log10()

        # print(logPlot)

    ## todo  use builtin method to create filesystem-compatible name
    fileNameLabel <- label %>%
        str_replace_all("!=", "ne") %>%
        str_replace_all(">", "gt") %>%
        str_replace_all("<", "lt") %>%
        str_replace_all(fixed("&"), "AND") %>%
        str_replace_all(fixed("|"), "OR") %>%
        str_replace_all(" ", "_")

#        ggsave(paste0("enrichmed_terms__", fileNameLabel, ".pdf"))
#        print(logPlot)

     tmpPng <- paste0(figDir, "/enrterms__", fileNameLabel, ".png")
     ggsave(tmpPng, logPlot, width=10, height = 2+round(nrow(enrResultsGrp)/5), limitsize=FALSE)
     data.frame(file=tmpPng)
})

#+ results="asis"
l_ply(term_barplot_files$file, function(pngFile){ cat(paste0("<img src='", pngFile, "'><br>"))})

########################################################################################################################
#' ## Enriched KEGG Pathways
#+ eval=(nrow(enrResults %>% filter(ontology=="kegg")) >0)

#' To understand spatio-temporal changes in gene expression better we now overlay enriched kegg pathways with the -log10(q_value) of each contrast. The direction of the expression changes is encoded as color, whereby red indicates that sample_1 is overexpressed. Because we have multiple contrasts of interest, this defines a slice-color barcode for each gene. To relate the barcode to contrasts we define the following slice order:

## todo why is tidyr not processing an empty dataframe
keggPathways <- enrResults %>% filter(ontology=="kegg") %$% ac(ID) %>% unique()

##+ results='asis'
#if(!exists("keggPathways") | nrow(keggPathways)==0){
#    cat("No enriched pathways found")
#}

#+ echo=FALSE
require.auto(pathview)
require.auto(png)

# keggPathways <- keggPathways[1]

#if(nrow(keggPathways)==0){
#    echo("no enriched kegg pathways were found in the dataset")
#}else{

#keggOrCode <- "mmu"
keggOrCode <- guess_pathview_species(geneLists$ensembl_gene_id)

## prepare p-value data
sliceData <- overlayData %>%
#    dcast(ensembl_gene_id ~ comparison, value.var="plot_score") %>%
    column2rownames("ensembl_gene_id")

#sliceData %>% head %>% kable()

data.frame(set=names(sliceData)) %>% mutate(slice_index=row_number()) %>% kable()

pwPlotDir <-".pathways"
dir.create(pwPlotDir)

plot_pathway <- function(pathwayID, overlayData){
#    pathwayID="mmu04015"
#    browser()
#    echo("processing pathway", pathwayID)
    pv.out <- pathview(
            gene.data = overlayData,
            pathway.id = pathwayID,
            species = keggOrCode,
#            out.suffix = pathwayID$kegg.description,
#            out.suffix = pathwayID,
            multi.state = ncol(overlayData)>1,
#            kegg.native=F,
#            node.sum = "mean", # the method name to calculate node summary given that multiple genes or compounds are mapped to it
            limit = list(gene = c(-4,4)),
            gene.idtype="ensembl"
    )

    outfile <- paste0(pathwayID, ".pathview", ifelse(ncol(overlayData)>1, ".multi", ""), ".png")

    ## move pathway plots into figures sub-directory
    figuresPlotFile <- file.path(".pathways", outfile)
    system(paste("mv", outfile, figuresPlotFile))

    system(paste("rm", paste0(pathwayID, ".xml"), paste0(pathwayID, ".png")))

    ## interactive plotting
#    ima <- readPNG(outfile)
#    plot.new()
#    lim <- par()
#    rasterImage(ima, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4])

    pv.out$plotfile=figuresPlotFile
    pv.out$pathway_id=pathwayID

    return(pv.out)
}

#keggPathways <- c("mmu04976", "mmu04972", "mmu04810", "mmu04520", "mmu04530", "mmu04270", "mmu04015")
#keggPathways <- c("mmu00830")



pathwayPlots <- keggPathways %>% llply(function(pathwayID){
    plot_pathway(pathwayID, sliceData)
})


save(pathwayPlots, file=".pathwayPlots.RData")
# pathwayPlots <- local(get(load("pathwayPlots.RData")))


## prepare tooltips with expression scores
ens2entrez <- quote({
    mart <- biomaRt::useDataset(guess_mart(geneLists$ensembl_gene_id), mart = biomaRt::useMart("ensembl"))
    biomaRt::getBM(attributes=c('ensembl_gene_id', 'entrezgene', 'external_gene_name'), mart=mart) %>% filter(!is.na(entrezgene))
}) %>% cache_it("ens2entrez") %>% distinct(ensembl_gene_id)
#unlen(ens2entrez$ensembl_gene_id)

toolTipData <- overlayData %>% left_join(ens2entrez)

makeTooltip <- function(entrez_id){
    toolTipData %>% filter(entrezgene==entrez_id) %>% dplyr::select(-entrezgene) %>% gather() %$% paste(key, value, sep=": ") %>% paste(collapse="\n") #%>% cat
}
#entrez_id=14679
#entrez_id=34234234234234
#makeTooltip("14679")



#+ results="asis", echo=FALSE

## simple non-clickable plots
## http://stackoverflow.com/questions/12588323/r-how-to-extract-values-for-the-same-named-elements-across-multiple-objects-of
#unlist(lapply(pathwayPlots, "[[", "plotfile"))
# unlist(lapply(pathwayPlots, "[[", "plotfile")) %>% l_ply(function(pngFile){ cat(paste0("<img src='", pngFile, "'><br>"))})

#cat("
#<style type='text/css'>
# img {
#   max-width: 100%;
# }
#</style>
#")

## todo add tooltips with additinal info
## http://stackoverflow.com/questions/5478940/how-to-create-a-html-tooltip-on-top-of-image-map

## extended version with clickable links
pathwayPlots %>% l_ply(function(plotData){
    #pngFile="mmu04015.pathview.png"
    #plotData <- pathwayPlots[[1]]
    #plotData <- unlist(pathwayPlots)
#    pathway_id=(basename(pngFile) %>% str_split_fixed("[.]", 2))[,1]
    pngFile <- plotData$plotfile
    pathway_id=plotData$pathway_id

    ## create link for image map
    keggNodes <- plotData$plot.data.gene %>%
        ## remove box offset
        mutate(x=x-22, y=y-8) %>% ## todo does not work for non-gene elements
        mutate(link=paste0("http://www.kegg.jp/dbget-bin/www_bget?", keggOrCode, ":", kegg.names )) %>%
        rowwise() %>% do({ curNode=.; mutate(as.df(curNode), tooltip=makeTooltip(as.integer(curNode$kegg.names)))})

    ## create tooltip by using mapping to


#    "http://www.kegg.jp/dbget-bin/www_bget?mmu:16412

    ## first the image itself
#    cat(paste0("<img usemap='#", pathway_id,"' src='", pngFile, "'><br>"))
#    cat(paste0("<p><div style='width: 2000px'><img style='float: left' usemap='#", pathway_id,"' src='", pngFile, "'></div><br>"))
    ## it seems that formatting works better without the left alignment
    cat(paste0("<p><div style='width: 2000px'><img usemap='#", pathway_id,"' src='", pngFile, "'></div><br>"))
    cat(paste0("<map name='", pathway_id,"'>"))
    #keggNodes %>% rowwise() %>% {curNode=.; cat(curNode$name)}

    #see http://www.html-world.de/180/image-map/
    keggNodes %>% a_ply(1, function(curNode){
        rectDef=with(curNode, paste(x, y, x+width, y+height, sep=","))
#        paste0("<area href='", curNode$link, "' alt='Text' coords='", rectDef , "' shape='rect'>") %>% cat()
        paste0("<area href='", curNode$link, "' title='", curNode$tooltip, "' alt='Text' coords='",rectDef , "' shape='rect'>") %>% cat()

    })
    cat("</map></p><br>")
})

## respin it for cild inclusion
# require(knitr); setwd("/Volumes/projects/bioinfo/scripts/ngs_tools/dev/common"); spin("cp_enrichment.R", knit=F)


#' ## Notes
#' Other interesting tools to perform enrichment testing
#' * [piano](http://www.bioconductor.org/packages/release/bioc/html/piano.html)