Hello,
I have a raw count data matrix (with genes in rows and samples in columns) that needs to be normalized. I can't use DESeq2 or Limma directly for this, since the design matrix is not fixed for my data. Specifically, one of the columns of the design matrix changes from gene to gene. So I need to do the normalization myself. Since I'm new to this, I'm not sure if I'm on the right track, so any help, comment, or reference would be much appreciated. Here's what I have in mind to do:
Suppose that the raw counts matrix is called "reads". Then using edgeR, I can do the following:
dge <- DGEList(counts=reads)
dge <- calcNormFactors(dge)
I have a couple of questions on this:
(1) is there a function in edgeR that directly outputs the matrix of normalized counts?
(2) If not, can I calculate the normalized count for each gene-sample combination with
dge$counts/(dge$samples$lib.size*dge$samples$norm.factors)?
I'd appreciate your help,
Golsheed
I have a raw count data matrix (with genes in rows and samples in columns) that needs to be normalized. I can't use DESeq2 or Limma directly for this, since the design matrix is not fixed for my data. Specifically, one of the columns of the design matrix changes from gene to gene. So I need to do the normalization myself. Since I'm new to this, I'm not sure if I'm on the right track, so any help, comment, or reference would be much appreciated. Here's what I have in mind to do:
Suppose that the raw counts matrix is called "reads". Then using edgeR, I can do the following:
dge <- DGEList(counts=reads)
dge <- calcNormFactors(dge)
I have a couple of questions on this:
(1) is there a function in edgeR that directly outputs the matrix of normalized counts?
(2) If not, can I calculate the normalized count for each gene-sample combination with
dge$counts/(dge$samples$lib.size*dge$samples$norm.factors)?
I'd appreciate your help,
Golsheed
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