Hi all!
I have some RNA-seq data with only one phenotype (disease) and no controls. I want to use voom to 'convert' the data to 'microarray' like data. Is this possible?
I do not want to do a differential expression analysis.
Best
Nina
Hi all!
I have some RNA-seq data with only one phenotype (disease) and no controls. I want to use voom to 'convert' the data to 'microarray' like data. Is this possible?
I do not want to do a differential expression analysis.
Best
Nina
Certainly. You don't ever need to specify a design matrix; voom() will by default just use a design matrix that only has an intercept term. From ?voom:
design: design matrix with rows corresponding to samples and columns to coefficients to be estimated. Defaults to the unit vector meaning that samples are treated as replicates.
Are you planning to analyze the output values using software tools that understand precision weights? If not, then you won't be fully using the voom output, so you may be better off using the cpm() or rpkm() functions of the edgeR package.
Edit: given your plan to use elastic net, I would use rpkm() with a large prior.count, say prior.count=5.
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Hi!
Thank you! I want to use elastic net with the output. My plan was to use the fpkm table with voom and then apply elastic net.
Nina