diffGenes: Performs differential analysis of chroGPS-genes maps based on...

In chroGPS: chroGPS2: Generation, visualization and differential analysis of epigenome maps

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)

chroGPS documentation built on Oct. 31, 2019, 4:52 a.m.