Question: error while using edgeR( Negative counts not allowed)
1
gravatar for deeksha3696
8 days ago by
deeksha369610
deeksha369610 wrote:

I am doing an RNAseq analysis. (using edgeR) I am loading my data for differential analysis, my data is of normalized gene expression(FPKM). I am now just trying to make DEGList object but, it is showing "Negative counts not allowed".

I have removed all negative values from the data, it is still showing the error(( Negative counts not allowed).

Please suggest what should i do. thank you!!

Command:

dgeFull <- DGEList(y, group = sampleInfo$condition)
Error: Negative counts not allowed
edger rna-seq R • 137 views
ADD COMMENTlink modified 8 days ago by zx87546.5k • written 8 days ago by deeksha369610
5
gravatar for WouterDeCoster
8 days ago by
Belgium
WouterDeCoster36k wrote:

my data is of normalized gene expression(FPKM).

You need raw counts for edgeR. Don't use any normalization.

ADD COMMENTlink written 8 days ago by WouterDeCoster36k

thanks from where can i get the raw counts

ADD REPLYlink written 8 days ago by deeksha369610
1

Download the data from NCBI or directly from ENA ( Fast download of FASTQ files and metadata from the European Nucleotide Archive (ENA) ).

ADD REPLYlink modified 8 days ago • written 8 days ago by ATpoint13k

From where did you get the fpkm counts...?

We are bad at reading your mind so you'll have to elaborate.

ADD REPLYlink written 8 days ago by WouterDeCoster36k

I downloaded the fpkm values from NCBI GEO DATA SET

ADD REPLYlink written 8 days ago by deeksha369610
1

FPKM values are not suitable for statistics, this is a recurrent discussion. See also edgeR user's guide, end of 2.3 on page 12. You need raw read counts, if they don't provide them at GEO, you can redo the analysis: download fastq files -> align them to reference e.g. with STAR -> counts reads with e.g. featureCounts

ADD REPLYlink modified 8 days ago • written 8 days ago by b.nota6.1k

While I don't completely agree that FPKM can't be used for any statistics (and I think it can be useful for visualization), it is true that you need raw counts for edgeR (and most RNA-Seq Bioconductor differential expression packages).

I also agree re-processing raw data can be frequently be helpful. Even if you end up with a similar result, you should understand the analysis process better and be more confident in your results :)

ADD REPLYlink written 7 days ago by Charles Warden6.1k

Please add a link to the dataset you are using. It may be necessary to download the reads and process those yourself.

ADD REPLYlink written 8 days ago by WouterDeCoster36k
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