Draw a heatmap with a "super large" matrix
I want to draw a heat map.
I have a 100k * 100k square matrix (50Gb (csv), numbers on the right side and others filled with 0).
I want to ask, "How can I draw a map with R?" with this huge dataset.
I am trying this code on a large RAM machine.
d = read.table("data.csv", sep=",")
d = as.matrix(d + t(d))
heatmap(d)
I tried some libraries like heatmap.2 (in gplots) or something like that. But they take so much time and memories.
source to share
What I suggest to you is to drastically reduce the sampling of your matrix before drawing the heatmap for example. making the average for each sub-matrix (as suggested by @IaroslavDomin):
# example of big mx 10k x 10 k
bigMx <- matrix(rnorm(10000*10000,mean=0,sd=100),10000,10000)
# here we downsample the big matrix 10k x 10k to 100x100
# by averaging each submatrix
downSampledMx <- matrix(NA,100,100)
subMxSide <- nrow(bigMx)/nrow(downSampledMx)
for(i in 1:nrow(downSampledMx)){
rowIdxs <- ((subMxSide*(i-1)):(subMxSide*i-1))+1
for(j in 1:ncol(downSampledMx)){
colIdxs <- ((subMxSide*(j-1)):(subMxSide*j-1))+1
downSampledMx[i,j] <- mean(bigMx[rowIdxs,colIdxs])
}
}
# NA to disable the dendrograms
heatmap(downSampledMx,Rowv=NA,Colv=NA)
Of course, with your huge matrix, it will take some time to compute downSampledMx, but it should be feasible.
EDIT:
I think the downsampling should preserve recognizable "macro patterns", for example. see the following example:
# create a matrix with some recognizable pattern
set.seed(123)
bigMx <- matrix(rnorm(50*50,mean=0,sd=100),50,50)
diag(bigMx) <- max(bigMx) # set maximum value on the diagonal
# set maximum value on a circle centered on the middle
for(i in 1:nrow(bigMx)){
for(j in 1:ncol(bigMx)){
if(abs((i - 25)^2 + (j - 25)^2 - 10^2) <= 16)
bigMx[i,j] <- max(bigMx)
}
}
# plot the original heatmap
heatmap(bigMx,Rowv=NA,Colv=NA, main="original")
# function used to down sample
downSample <- function(m,newSize){
downSampledMx <- matrix(NA,newSize,newSize)
subMxSide <- nrow(m)/nrow(downSampledMx)
for(i in 1:nrow(downSampledMx)){
rowIdxs <- ((subMxSide*(i-1)):(subMxSide*i-1))+1
for(j in 1:ncol(downSampledMx)){
colIdxs <- ((subMxSide*(j-1)):(subMxSide*j-1))+1
downSampledMx[i,j] <- mean(m[rowIdxs,colIdxs])
}
}
return(downSampledMx)
}
# downsample x 2 and plot heatmap
downSampledMx <- downSample(bigMx,25)
heatmap(downSampledMx,Rowv=NA,Colv=NA, main="downsample x 2")
# downsample x 5 and plot heatmap
downSampledMx <- downSample(bigMx,10)
heatmap(downSampledMx,Rowv=NA,Colv=NA, main="downsample x 5")
Here are three heatmaps:
source to share