R/DGEExact-methods.R

Defines functions DGEExact.to.html DGEExact.to.data.frame

setMethod("publish",
    signature = signature(
        object = "DGEExact",
        publicationType = "HTMLReport"
    ),
    def = function(object, publicationType, countTable, conditions,
        annotation.db = 'org.Hs.eg', n = 1000,
        pvalueCutoff = 0.01, lfc = 0, adjust.method = 'BH', 
        sort.method = 'p.value', ...){
        ## First, make a data.frame for publication,
        ## then call publish on that data.frame
        df <- .DGEExact.to.html(object, publicationType, as.matrix(countTable), 
            conditions, annotation.db = annotation.db, n = n, pvalueCutoff = pvalueCutoff, lfc = lfc, 
            adjust.method = adjust.method, sort.method = sort.method, ...)
        publish(df, publicationType, ...)
    }
)


.DGEExact.to.data.frame <- function(object, annotation.db = 'org.Hs.eg', 
    pvalueCutoff = 0.01, n = 1000, lfc = 0, adjust.method='BH', 
    sort.method = 'p.value', make.plots = FALSE, ...)
{
    dat <- topTags(object, n = n, adjust.method = adjust.method, 
        sort.by = sort.method)

    if(!is.null(object$genes) & all(rownames(object$genes) == 
            as.character(seq_along(rownames(object$genes))))){
        selection <- as.numeric(rownames(dat$table))
        selection <- rownames(object$table)[selection]
    } else {
        selection <- rownames(dat$table)
    }
    
    ##The following gives you all pvalues
    padj <- p.adjust(object$table$PValue, method = adjust.method)
    padj <- padj[match(selection, rownames(object$table))]
    dat <- data.frame(dat$table, padj)
    
    dat.adj <- dat[dat$padj < pvalueCutoff,]
    dat.lfc <- dat.adj[abs(dat.adj$logFC) > abs(lfc), ]
    
    
    if(length(rownames(dat.lfc)) == 0)
        stop("No genes meet the selection criteria. Try changing the log-fold change or p-value cutoff.")
    
    
    ann.map.available <- tryCatch(getAnnMap("SYMBOL", annotation.db), 
        error=function(e){ return(FALSE) })

    if (inherits(ann.map.available, "AnnDbBimap")){
        ## Check valid Entrez ids are passed in
        check.eg.ids(rownames(dat), annotation.db)
        fdata <- annotate.genes(rownames(dat), annotation.db,
            keytype = "ENTREZID", columns = list(EntrezId = "ENTREZID", 
                Symbol = "SYMBOL", GeneName = "GENENAME"))
        
    } else {
        if(!is.null(object$genes)){
            fdata <- object$genes[match(selection, rownames(object$table)), , 
                drop = FALSE]
            if(!all(rownames(fdata) == selection))
                rownames(fdata) <- selection
        } else {
            IDs <- rownames(dat.lfc)
            fdata <- data.frame(IDs, stringsAsFactors = FALSE)
            rownames(fdata) <- IDs
        }
    }
    
    if(make.plots){
        ret <- data.frame(
            fdata,
            Image = rep("", nrow(fdata)),
            dat.lfc$logFC,
            dat.lfc$padj,
            stringsAsFactors = FALSE
        )
    } else {
        ret <- data.frame(
            fdata,
            dat.lfc$logFC,
            dat.lfc$padj,
            stringsAsFactors = FALSE        
        )
    }

    colnames(ret)[which(colnames(ret) == 'dat.lfc.logFC')] <- 'logFC'
    colnames(ret)[which(colnames(ret) == 'dat.lfc.padj')] <- 'Adjusted p-Value'
    
    return(ret)
}


.DGEExact.to.html <- function(object, htmlRep, countTable, conditions, 
    annotation.db = 'org.Hs.eg', pvalueCutoff = 0.01, n = 1000, lfc = 0, 
    adjust.method = 'BH', sort.method = 'p.value', make.plots = TRUE, ...)
{
    dat <- topTags(object, n = n, adjust.method = adjust.method, sort.by = sort.method)

    ## Check valid Entrez ids are passed in
    check.eg.ids(rownames(dat), annotation.db)
    
    selection <- as.numeric(rownames(dat$table))
    ##The following gives you all pvalues
    padj <- p.adjust(object$table$PValue, method = adjust.method)
    padj <- padj[match(selection, rownames(object$table))]
    dat <- data.frame(dat$table, padj)

    dat.adj <- dat[dat$padj < pvalueCutoff,]
    dat.lfc <- dat.adj[abs(dat.adj$logFC) > abs(lfc), ]

    if(length(rownames(dat.lfc)) == 0)
        stop("No genes meet the selection criteria. Try changing the log-fold change or p-value cutoff.")
  
    ann.map.available <- tryCatch(getAnnMap("SYMBOL", annotation.db), 
        error=function(e){ return(FALSE)})
    
    if (inherits(ann.map.available, "AnnDbBimap")){
      fdata <- data.frame(
        EntrezId = unlist(rownames(dat.lfc)),
        Symbol = unlist(mget(rownames(dat.lfc), 
            getAnnMap("SYMBOL", annotation.db), ifnotfound = NA)),
        GeneName = unlist(mget(rownames(dat.lfc), 
            getAnnMap("GENENAME", annotation.db), ifnotfound = NA)),
        stringsAsFactors = FALSE
      )

    fdata$EntrezId <- hwrite(fdata$EntrezId, 
        link=paste("http://www.ncbi.nlm.nih.gov/gene/", fdata$EntrezId, sep=''), table=FALSE)
      
    } else {
      IDs <- rownames(dat.lfc)
      fdata <- data.frame(IDs, stringsAsFactors = FALSE)
    }    
    
    
    if( ! make.plots ){
        ret <- data.frame(
            fdata,
            dat.lfc$logFC,
            dat.lfc$padj,
            stringsAsFactors = FALSE
        )
    } else {
        ret <- data.frame(
            fdata,
            Image = rep("", nrow(fdata)),
            dat.lfc$logFC,
            dat.lfc$padj,
            stringsAsFactors = FALSE
        )

        ## log2 transform count data and add one for psuedocount
        countTable <- log2(countTable + 1)
    
        figures.dirname <- paste('figures', name(htmlRep), sep='')  
        figure.directory <- file.path(basePath(htmlRep), 
            reportDirectory(htmlRep), figures.dirname)
        .safe.dir.create(figure.directory)
        
        .make.gene.plots(ret, countTable, conditions, figure.directory,
            ylab.type = "(log2 counts per million)")
        
        mini.image <- file.path(figures.dirname, 
            paste("mini", rownames(dat.lfc), "png", sep="."))
        pdf.image <- file.path(figures.dirname, 
            paste("boxplot", rownames(dat.lfc), "pdf", sep="."))
        ret$Image <- hwriteImage(mini.image, link=pdf.image, table=FALSE)
    
    }
    
    colnames(ret)[which(colnames(ret) == 'dat.lfc.logFC')] <- 'logFC'
    colnames(ret)[which(colnames(ret) == 'dat.lfc.padj')] <- 'Adjusted p-Value'
    
    return(ret)
}
Bioconductor-mirror/ReportingTools documentation built on July 28, 2017, 4:28 a.m.