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I have RNA seq data of raw gene counts that I want to transform for linear modelling. I am trying to voom transform, to do a weighted analysis.

Data frame: prac_count_10

gene         S1     S2    S3   S4    S5 
ENG0000456   0      1     10   145   24
ENG0000458   7      2     9    0     0
ENG0000657   76     12    56   10    2 
ENG0000689   0      0     0    3     5

Code:

prac_prac_10 <- voom(prac_count_10[,2:5], design=NULL, lib.size=NULL, 
  normalize.method = "none", span = 0.5

However this doesn't give an output with the gene sample names and logCPM values. Would like an output with gene id's and logCPM values for linear modelling.

Also tried:

cpm <- cpm(prac_count_10)
lcpm <- cpm(prac_count_10, log=TRUE)


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Try to use DElist() function before you transform, and also make rownames first.

counts <- prac_count_10[,-1]

rownames(counts) <- prac_count_10[,1]

DGE1 <- DGEList(counts)

And then continue with your voom...

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  • $\begingroup$ Thank you. what package is DElist in? $\endgroup$ – holly Apr 9 at 8:21
  • $\begingroup$ That would be edgeR. $\endgroup$ – benn Apr 9 at 8:23
  • $\begingroup$ If I do: prac_count <- data.frame(prac_count, row.names=1), counts<- prac_count[,1], rownames(counts) <- prac_count[,1], DGE1 <-DGEList(counts) If doesn't work... have I missed a step? Thank you so much $\endgroup$ – holly Apr 9 at 8:31
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    $\begingroup$ That's not the code I gave in my answer, is it? $\endgroup$ – benn Apr 9 at 8:48
  • $\begingroup$ Okay, thank you for your kind help $\endgroup$ – holly Apr 9 at 10:37

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