I am trying to make a heatmap and the was able to achieve the one I wanted. However, I have a feeling it looks clumpsy. Y is a 4 column matrix with expression values
y<-ave_scaledCounts[Gene_Markers,]
hr <- hclust(as.dist(1-cor(t(y), method="pearson")), method="centroid");
hc <- hclust(as.dist(1-cor(y, method="spearman")), method="centroid")
# Cuts the tree and creates color vector for clusters.
mycl <- cutree(hr,k=4, h=max(hr$height)/2);
mycolhc <- rainbow(length(unique(mycl)), start=0.1, end=0.9); mycolhc <- mycolhc[as.vector(mycl)]
myheatcol <- bluered(75)
pdf("Hierarchial_Clustering/Final_HeatMap Hierarchial clustering.pdf",width=9,height=7)
heatmap.2(y, Rowv=as.dendrogram(hr), Colv=as.dendrogram(hc), col=myheatcol, scale="row", density.info="none", trace="none",
+ RowSideColors= mycolhc,margins=c(10,10),keep.dendro=FALSE)
dev.off()
. I would like the heatmap in itself to be smaller in area... preferable like a tile!! The dendograms are not neccessary and the color key could be even thinner! ANy help would be great! Thanks!
EDIT: Would it just be possible to make a better heatmap if I just have the clusters in order already?? May be ggplot2 package
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k.nirmalraman
Perhaps you could try myheatcol <- colorpanel(3, "#0000ff", "#ffffff", "#ff0000") or myheatcol <- bluered(3) to "force" each cell to fall into one of three colors.
Even narrower would be myheatcol <- colorpanel(2, "#0000ff", "#ff0000") or myheatcol <- bluered(2). In that case, a cell is either blue or red.
This probably won't work too well for smaller clusters, but it should work okay for larger ones, I'd think.
If you want to keep a blue-to-white gradient for values from -1.5 to 0 and have all z-scores greater than 0 be painted one shade of red, you could construct a color palette vector like so:
> myheatcol.bluewhite <- colorpanel(128, "#0000ff", "#ffffff")
> myheatcol.red <- rep("#0000ff", 127)
> myheatcol <- c(myheatcol.bluewhite, myheatcol.red)
Thank you!! Actually, the color gradient is just fine.. I would however like to know how can I
I tried dendogram="none" but did not help :( The problem is I am loosing the clusters if I do not include rowv and colv...
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clearly this scale is not appropriate for correlations. wouldn't you normally calculate some kind of distance value?
I thought I was doing it here
as.dist(1-cor(t(y)Do you mean the color scale is not appropriate? If you dont mind could you expand a bit more??
have you tried just euclidean distances on the raw data?
This just made a huge square with same color, I mean the clusters cannot be seen. I added
col=myheatcol"