Dear All,
I am trying to get correlation between Diseases (80) in columns and
samples in rows (UNEQUAL) using gene expression (at less 1000,numeric). For
this I can use CORREP package with cor.unbalanced function.
But before to get this final matrix I need to load and to store the
expression of 1000 genes for every Disease (80). Every disease has
different number of samples (between 50 - 500).
It is possible to get a cube of matrices with equal columns but unequal
rows? I think NO and I can't use array function.
I am trying to get à list of matrices having the same number of columns but
different number of rows. as
Cubist <- vector("list", 1)
Cubist$Expression <- vector("list", 1)
for (i in 1:80){
matrix <- function(getGeneExpression[i])
Cubist$Expression[[Disease[i]]] <- matrix
}
At this step I have:
length(Cubist$Expression)
#80
dim(Cubist$Expression$Disease1)
#526 1000
dim(Cubist$Expression$Disease2)
#106 1000
names(Cubist$Expression$Disease1[4])
#ABD
names(Cubist$Expression$Disease2[4])
#ABD
Now I need to built the final matrices for every genes (1000) that I will
use for CORREP function.
Is there a way to extract directly the first column (first gene) for all
Diseases (80) from Cubist$Expression? or
I need to built 1000 matrices with 80 columns and unequal rows?
Cublist$Diseases <- vector("list", 1)
for (k in 1:1000){
for (i in 1:80){
Cublist$Diseases[[gene[k] ]] <- Cubist$Expression[[Diseases[i] ]][k]
}
}
This double loops is time consuming...Is there a way to do this faster?
Thanks,
karim
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