Question: How to Normalize and log tranform the miRNA Seq data using DeSeQ2 / EdgeR?
0
gravatar for prithvi.mastermind
16 days ago by
prithvi.mastermind0 wrote:

I wish to obtain log-transformed and upper quantile normalized expression data for my miRNA-Seq raw count data. How should I implement the script using EdgeR or DeSEQ2? The default option in DeSEQ2 is TMM normalization. Is there a way out for obtaining upper-quartile as in EdgeR? How can i accommodate in my script.

rawCountTable <- read.delim(file.choose(), row.names=1)                                   ## miRNA-Seq raw data##
Col_data = read.table(file = "COL_LUAD_miRNA.txt", header = T, sep = "\t")     ## miRNA-Seq Annotation data##
dgeFull <- DGEList(rawCountTable, group = Col_data$Condition)
dgeFull <- DGEList(dgeFull$counts[apply(dgeFull$counts, 1, sum) != 0, ], group=dgeFull$samples$group)
dgeFull <- calcNormFactors(dgeFull, method="upperquartile")
dgeFull <- estimateCommonDisp(dgeFull)
normCounts <- cpm(dgeFull, log=TRUE, prior.count = 0.25)
## Perform batch correction of normalized and log transformed values##

After implementing this script in EdgeR I'm obtaining some negative values in my expression data at the end.

rna-seq R software error • 87 views
ADD COMMENTlink modified 16 days ago by Gordon Smyth1.8k • written 16 days ago by prithvi.mastermind0

You obtain some negative values because you log transform count >0.25. Every log transformation from 0.25 to 1 will be end up negative. Try to set up prior.count to 1.

ADD REPLYlink written 16 days ago by Bastien Hervé4.7k
0
gravatar for Gordon Smyth
16 days ago by
Gordon Smyth1.8k
Australia
Gordon Smyth1.8k wrote:

Your script already does what you say you want. Negative values on the log-scale are not a problem, although you could reduce the number of negative values by leaving prior.count at the default value instead of setting it so small.

ADD COMMENTlink written 16 days ago by Gordon Smyth1.8k
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