diffGenes {chroGPS} | R Documentation

## Performs differential analysis of chroGPS-genes maps based on Bayesian Correct Classification Rates.

### Description

The function uses Bayesian posterior probability of correct classification (CCR) of chroGPS-genes maps as computed by the `clusGPS` function to identify statistically significant epigenetic changes between two different backgrounds (technical and/or biological). The functions `find.fdr` and `find.threshold` are used internally to compute approximated FDRs from a vector of input posterior probabilities of correct classification (CCRs).

### Usage

    diffGenes(xy,m,clus,label.x,label.y,clusName=NULL,fdr=TRUE,mc.cores=1)

### Arguments

`xy` | A `matrix` or `data.frame` object, containing a combined matrix with whole-genome epigenetic information for backgrounds X and Y, as obtained by the `combineGenesMatrix` function.
`m` | A `mds` object, containing a chroGPS-genes MDS map, obtained for the combined matrix xy.
`clus` | A `clusGPS` object, containing a chroGPS-genes cluster solution, obtained for the provided background via the `clusGPS` and `mergeClusters` functions.
`label.x` | Name for background X, by default 'mds1'
`label.y` | Name for background Y, by default 'mds2'
`clusName` | If `clus` contains more than one clustering solution (i.e. `cutree` dendrogram height cut), the name of the selected one. If no name is provided, the first element is used.
`fdr` | Set to TRUE (default) to compute False Discovery Rate of cluster correct classification from posterior probabilities.
`mc.cores` | Number of cores to use for parallel computations.

### Value

The function returns a data frame with posterior probabilities and FDRs of cluster classifications for all genes in backgrounds X and Y, so that significant changes can be traced and reported easily.

### Author(s)

Oscar Reina.

### See Also

See functions `distGPS, mds, clusGPS, profileClusters` for generating chroGPS-genes maps. See function `plotTransitions` for examples on how to visualize differential gene maps results.

### Examples

    # Summarize factor replicates with method 'any' so that 1 replicate
    #  having the mark is enough
    # Assuming s2.tab and bg3.tab contain the full datasets for dm3 genome
    #  and all factors used in Font-Burgada et al.
    # s2.tab <- mergeReplicates(s2.tab,s2names$Factor,'any')
    # bg3.tab <- mergeReplicates(bg3.tab,bg3names$Factor,'any')

    # Join, use common factors. Then use common genes only from those that
    #  have at least one mark in both s2 and bg3
    # x <- combineGenesMatrix(s2.tab,bg3.tab,'S2','BG3')

    # Build map and cluster as always
    # d <- distGPS(x,metric='tanimoto',uniqueRows=TRUE)

    # Not run
    # m <- mds(d,type='classic',splitMDS=TRUE,split=0.16,mc.cores=4)
    # mm <- mds(d,m,type='boostMDS',samplesize=0.005,mc.cores=6)
    # unique(rownames(m@points)==rownames(mm@points)) # sanity check
    # This should be incorporated in the function code...
    #m@points <- m@points[rownames(d@d),]
    #mm@points <- mm@points[rownames(d@d),]

    # Cluster
    # h <- hclust(as.dist(d@d),method='average')
    # clus <-
    # clusGPS(d,mm,h,k=max(cutree(h,h=0.5)),ngrid=10000,mc.cores=8,recalcDist=FALSE,verbose=FALSE)
    # clus.merged <- mergeClusters(clus,brake=0,mc.cores=8)
    # clus
    # clus.merged

    # Use new function to profile clusters
    # pc <- profileClusters2(x,clus.merged,normalize=TRUE)
    # pheatmap(pc,trace='none',scale='none',col=bluered(100))

    # Perform differential analysis
    # x.diff <- res <-
    # diffGPS.clus(x,mm,clus.merged,label.x='S2',label.y='BG3',clusName=clusNames(clus.merged)[1],fdr=TRUE,mc.cores=8)
    # write.csv(x.diff,'diffgenes_fdrest.csv')

    # Select genes changing clusters with FDR 0.05
    # xx.diff <- x.diff[x.diff$ClusID.S2!=x.diff$ClusID.BG3 &
    # x.diff$FDR.S2<0.25 & x.diff$FDR.BG3<0.25,]
    # xx.diff$CC <- paste(xx.diff$ClusID.S2,xx.diff$ClusID.BG3,sep='.')
    # head(sort(table(xx.diff$CC),decreasing=TRUE))
    # write.csv(xx.diff,'kk_fdrest2.csv')

    # Perform enrichment test using getEnrichedGO from chippeakanno package
    # library(ChIPpeakAnno)
    # library(org.Dm.eg.db)

    # enriched.GO <- lapply(c('2.9','5.2'),function(cc) {
    #        fbid <- as.character(xx.diff$geneid[xx.diff$CC==cc])
    #            if (length(fbid)>=25)
    #                        ans <-
    #        getEnrichedGO(annotatedPeak=fbid,orgAnn='org.Dm.eg.db',maxP=0.05,multiAdjMethod='BH')
    #            else ans <- NULL
    #            return(ans)
    #    })

    # names(enriched.GO) <- c('2.9','5.2')
    # enriched.GO <- enriched.GO[unlist(lapply(enriched.GO,length))>0]
    # enriched.GO <- lapply(enriched.GO,function(x) lapply(x,function(y)
    # unique(y[,-ncol(y)])))
    # lapply(enriched.GO,head)

    # Plot results with diffGPS.plot function
    # res.sel <- res[res$ClusID.S2!=res$ClusID.BG3,]

    # Plot
    # diffGenes.plot(x,mm,clus.merged,res.sel,transitions='10.2',label.x='S2',label.y='BG3',fdr1=0.25,fdr2=0.25)

[Package chroGPS version 2.2.0 Index]