hpGeneListComp {PCAN} | R Documentation

## HP semantic similarity for a whole gene list.

### Description

This function compare a whole gene list to a set of HP terms using a matrix of semantic similarity.

### Usage

    hpGeneListComp(geneList, ssMatByGene, geneSSScore = NULL, ...)

### Arguments

`geneList` | a vector providing the genes of interest.
`ssMatByGene` | a list (one element per gene) of matrix of semantic similarity between HP terms as returned by `compareHPSets`. This list has to be unbiased in order to compute p-values.
`geneSSScore` | a vector of semantic similarity scores for all the genes in ssMatByGene list. If not provided these scores are computed from ssMatByGene.
`...` | parameters for `hpSetCompSummary` if geneSSScore is not provided.

### Value

A list with the following elements:

hpoi

  The original HP of interest.

allScoreDist

  The distribution of scores for all genes for the HP of interest.

scores

  The semantic similarity by gene.

best.matches

  For each gene which related HP terms best fits with the HP of interest (colnames of the elements of ssMatByGene).

median

  The median of scores.

p.value

  According to a `wilcox.test` comparing genes of interest to all the other genes.

best.gene

  Gene with the highest score among the genes of interest.

max

  Maximum score.

score.quantiles

  Quantile of the scores compared to the whole list of gene.

adj.quant

  Adjusted quantiles according Benjamini Hochberg (`link{p.adjust}`).

### Author(s)

Patrice Godard

### See Also

`hpGeneHeatmap`, `compareHPSets`, `hpSetCompSummary` and `hpSetCompBestMatch`

### Examples

    data(geneByHp, hp_descendants, package="PCAN")
    data(hp_ancestors, hpDef, package="PCAN")
    data(traitDef, geneDef, package="PCAN")
    data(hpByTrait, package="PCAN")
    geneByHp <- unstack(geneByHp, entrez~hp)

    ###########################################
    ## Compute information content of each HP according to associated genes
    ic <- computeHpIC(geneByHp, hp_descendants)

    ###########################################
    ## Use case: comparing a gene and a disease
    omim <- "612285"
    traitDef[which(traitDef$id==omim),]
    entrez <- "57545"
    geneDef[which(geneDef$entrez==entrez),]
    ## Get HP terms associated to the disease
    hpOfInterest <- hpByTrait$hp[which(hpByTrait$id==omim)]
    ## Get HP terms associated to the gene
    hpByGene <- unstack(stack(geneByHp), ind~values)
    geneHps <- hpByGene[[entrez]]
    ## HP Comparison
    hpGeneResnik <- compareHPSets(
        hpSet1=names(ic), hpSet2=hpOfInterest,
        IC=ic,
        ancestors=hp_ancestors,
        method="Resnik",
        BPPARAM=SerialParam()
    )
    hpMatByGene <- lapply(
        hpByGene,
        function(x){
            hpGeneResnik[x, , drop=FALSE]
        }
    )
    resnSss <- unlist(lapply(
        hpMatByGene,
        hpSetCompSummary,
        method="bma", direction="symSim"
    ))
    candScore <- resnSss[entrez]

    ###########################################
    ## The pathway consensus approach
    ## What about genes belonging to the same pathways as the candidate
    data(rPath, hsEntrezByRPath, package="PCAN")
    candPath <- names(hsEntrezByRPath)[which(unlist(lapply(
        hsEntrezByRPath,
        function(x) entrez %in% x
    )))]
    rPath[which(rPath$Pathway %in% candPath),]
    rPathRes <- hpGeneListComp(
        geneList=hsEntrezByRPath[[candPath]],
        ssMatByGene = hpMatByGene,
        geneSSScore = resnSss
    )
    hist(
        resnSss,
        breaks=100, col="grey",
        ylim=c(0,5),
        xlab=expression(Sim[sym]),
        ylab="Density",
        main=paste(
            "Distribution of symmetric semantic similarity scores for all the",
            length(resnSss), "genes"
        ),
        probability=TRUE
    )
    toAdd <- hist(
        rPathRes$scores,
        breaks=100,
        plot=FALSE
    )
    for(i in 1:length(toAdd$density)){
        polygon(
            x=toAdd$breaks[c(i, i+1, i+1, i)],
            y=c(0, 0, rep(toAdd$density[i], 2)),
            col="#BE000040",
            border="#800000FF"
        )
    }
    legend(
        "topright",
        paste0(
            "Genes belonging to the ", candPath," pathway:\n'",
            rPath[which(rPath$Pathway %in% candPath),"Pathway_name"],
            "'\nand with a symmetric semantic similarity score (",
            sum(!is.na(rPathRes$scores)),
            "/",
            length(rPathRes$scores),
            ")\n",
            "p-value: ", signif(rPathRes$p.value, 2)
        ),
        pch=15,
        col="#BE000040",
        bty="n",
        cex=0.6
    )
    ## Assessing the symmetric semantic similarity for each gene in the pathway
    pathSss <- rPathRes$scores[which(!is.na(rPathRes$scores))]
    names(pathSss) <- geneDef[match(names(pathSss), geneDef$entrez), "symbol"]
    opar <- par(mar=c(7.1, 4.1, 4.1, 2.1))
    barplot(
        sort(pathSss),
        las=2,
        ylab=expression(Sim[sym]),
        main=rPath[which(rPath$Pathway %in% candPath),"Pathway_name"]
    )
    p <- c(0.25, 0.5, 0.75, 0.95)
    abline(
        h=quantile(resnSss, probs=p),
        col="#BE0000",
        lty=c(2, 1, 2, 2),
        lwd=c(2, 2, 2, 1)
    )
    text(
        rep(0,4),
        quantile(resnSss, probs=p),
        p,
        pos=3,
        offset=0,
        col="#BE0000",
        cex=0.6
    )
    legend(
        "topleft",
        paste0(
            "Quantiles of the distribution of symmetric semantic similarity\n",
            "scores for all the genes."
        ),
        lty=1,
        col="#BE0000",
        cex=0.6
    )
    par(opar)

    ## A heatmap showing the best HP match for each gene in the pathway
    geneLabels <- geneDef$symbol[which(!duplicated(geneDef$entrez))]
    names(geneLabels) <- geneDef$entrez[which(!duplicated(geneDef$entrez))]
    hpLabels <- hpDef$name
    names(hpLabels) <- hpDef$id
    hpGeneHeatmap(
        rPathRes,
        genesOfInterest=entrez,
        geneLabels=geneLabels,
        hpLabels=hpLabels,
        clustByGene=TRUE,
        clustByHp=TRUE,
        palFun=colorRampPalette(c("white", "red")),
        goiCol="blue",
        main=rPath[which(rPath$Pathway %in% candPath),"Pathway_name"]
    )

    ###########################################
    ## What about genes interacting with the candidate (including itself)
    data(hqStrNw, package="PCAN")
    neighbors <- unique(c(
        hqStrNw$gene1[which(hqStrNw$gene2==entrez)],
        hqStrNw$gene2[which(hqStrNw$gene1==entrez)],
        entrez
    ))
    neighRes <- hpGeneListComp(
        geneList=neighbors,
        ssMatByGene = hpMatByGene,
        geneSSScore = resnSss
    )
    hist(
        resnSss,
        breaks=100, col="grey",
        ylim=c(0,10),
        xlab=expression(Sim[sym]),
        ylab="Density",
        main=paste(
            "Distribution of symmetric semantic similarity scores for all the",
            length(resnSss), "genes"
        ),
        probability=TRUE
    )
    toAdd <- hist(
        neighRes$scores,
        breaks=100,
        plot=FALSE
    )
    for(i in 1:length(toAdd$density)){
        polygon(
            x=toAdd$breaks[c(i, i+1, i+1, i)],
            y=c(0, 0, rep(toAdd$density[i], 2)),
            col="#BE000040",
            border="#800000FF"
        )
    }
    legend(
        "topright",
        paste0(
            "Genes interacting with ",
            geneDef[which(geneDef$entrez==entrez),"symbol"],
            " (", entrez, ")",
            "\nand with a symmetric semantic similarity score (",
            sum(!is.na(neighRes$scores)),
            "/",
            length(neighRes$scores),
            ")\n",
            "p-value: ", signif(neighRes$p.value, 2)
        ),
        pch=15,
        col="#BE000040",
        bty="n",
        cex=0.6
    )

    ## Assessing the symmetric semantic similarity score for each interacting gene
    neighSss <- neighRes$scores[which(!is.na(neighRes$scores))]
    names(neighSss) <- geneDef[match(names(neighSss), geneDef$entrez), "symbol"]
    opar <- par(mar=c(7.1, 4.1, 4.1, 2.1))
    barplot(
        sort(neighSss),
        las=2,
        ylab=expression(Sim[sym]),
        main=paste0(
            "Genes interacting with ",
            geneDef[which(geneDef$entrez==entrez),"symbol"],
            " (", entrez, ")"
        )
    )
    p <- c(0.25, 0.5, 0.75, 0.95)
    abline(
        h=quantile(resnSss, probs=p),
        col="#BE0000",
        lty=c(2, 1, 2, 2),
        lwd=c(2, 2, 2, 1)
    )
    text(
        rep(0,4),
        quantile(resnSss, probs=p),
        p,
        pos=3,
        offset=0,
        col="#BE0000",
        cex=0.6
    )
    legend(
        "topleft",
        paste0(
            "Quantiles of the distribution of symmetric semantic similarity\n",
            "scores for all the genes."
        ),
        lty=1,
        col="#BE0000",
        cex=0.6
    )
    par(opar)

    ## A heatmap showing the best HP match for each neighbor gene
    hpGeneHeatmap(
        neighRes,
        genesOfInterest=entrez,
        geneLabels=geneLabels,
        hpLabels=hpLabels,
        clustByGene=TRUE,
        clustByHp=TRUE,
        palFun=colorRampPalette(c("white", "red")),
        goiCol="blue",
        main=rPath[which(rPath$Pathway %in% candPath),"Pathway_name"]
    )

[Package PCAN version 1.16.0 Index]