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normalized rna seq data

My TMM normalized RNA seq data has some positive values ,zero values and non zero values .Now what does these three types of values indicates?how i will know that a gene is expressed or not or differently expressed?

rna-seq rna-seq gene
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written by maryak • maryak

Please describe in more detail what you did. Also, read carefully the edgeR User Guide and DESeq2 vignette, they have plenty of information on how to perform differential expression analysis.

My TMM normalized RNA seq data has some positive values ,zero values and non zero values

Positive values are non-zero. What you shouldn't see is negative values. All values are corrected counts of reads mapping to genes.

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written by h.mon h.mon

i have negative values as well.Can you please guide me what might went wrong?

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written by maryak • maryak

Perhaps you should share then what you did?

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written by WouterDeCoster WouterDeCoster 
    library(edgeR)
    RNAseq2 <-read.delim("C:\\Users\\hp \\Desktop\\BRCA.tsv",header = TRUE)
    rnames <-RNAseq2[,1]
    MA <- data.matrix(RNAseq2[,2:49])
    MAA  <- (2^MA)-1
   dge <- DGEList(MAA)
    dim(dge)
    #getCounts(dge)
    cal <- calcNormFactors(dge,method = "TMM")
    RR <- cpm(cal,normalized.lib.sizes=TRUE ,log = TRUE,prior.count = 1)
    hist(RR)
    row.names(RR)<- rnames
    #head(RR)
    write.table(RR,"C:\\Users\\hp folio\\Desktop\\TMM3.tsv",sep='\t',row.names=TRUE,col.names = TRUE)
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written by maryak • maryak

edgeR expects raw counts as input, not transformed data. You should do:

dge <- DGEList( data.matrix(RNAseq2[,2:49]) )
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written by h.mon h.mon

Actually my raw count data was log 2 transformed so i reversed the transformation in order to get original counts

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written by maryak • maryak

It would be much better if you can get access to the original data without having to tamper with it like this.

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written by WouterDeCoster WouterDeCoster

You do not actually do that... you return it to the linear scale and then subtract 1

MAA  <- (2^MA)-1

If your input data is log2-transformed, then you just need to do:

MAA  <- 2^MA

This may explain the negative counts

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written by Kevin Blighe Kevin Blighe

sorry i missed to mention that the data is log2(count+1) transformed... so that why i did

MAA  <- (2^MA)-1

but still unable to figure out wheres the problem

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written by maryak • maryak

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