1 | HeatmapCluster(obj, top)
|
obj |
The list objective from the function getClusterGene |
top |
The number of gene which showed top cluster specificity |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | data(melanoma)
melanoma1=as.matrix(melanoma[,2:dim(melanoma)[2]])
row.names(melanoma1)=melanoma[,1]
res=ModalFilter(data=melanoma1,geneK=10,cellK=10)
res=GeneFilter(obj=res)
res=getMarker(obj=res,MNN=200,MNNIndex=20)
res=SCcluster(obj=res)
res=getClusterGene(obj=res,method="Seurat")
HeatmapCluster(obj=res,top=10)
## The function is currently defined as
HeatmapCluster<-function(obj,top,scale="none"){
feature=obj$clustergene
topmarker=as.data.frame(feature
topmarker=topmarker[order(topmarker$clusts),]
method=obj$method
if (method=="Seurat"){
cluster=obj$seuratCluster
}else if (method=="dbscan"){
cluster=obj$dbscanCluster
}
clustertype=levels(factor(cluster))
genemean=c()
for (i in clustertype){
cell=names(cluster[cluster==i])
subdata=obj$rawdata[topmarker$gene,cell]
genemean=cbind(genemean,apply(subdata,1,mean))
}
row.names(genemean)=topmarker$gene
colnames(genemean)=clustertype
mat_col <- data.frame(cluster=levels(factor(cluster)))
row.names(mat_col) <- levels(factor(cluster))
mat_colors <- list(cluster=rainbow(length(clustertype)))
names(mat_colors$cluster)=levels(factor(cluster))
pheatmap(genemean,cluster_cols = F,scale=scale,cluster_rows = F,show_colnames=F,annotation_col = mat_col,annotation_colors = mat_colors)
}
{ ~kwd1 }
{ ~kwd2 }
|
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