Question: Output from featureCounts() as input to DESeq2
0
gravatar for Elizabeth Sam
2.3 years ago by
India
Elizabeth Sam0 wrote:

I am new to RNA-seq. I plan to find out the differentially expressed genes from two samples. For that I first downloaded the fastq files and aligned the reads using align(). Later, the gene level expression values were summarized as integer number using featureCounts() . Can I give this as input to DeSeq2.? If yes, then what steps to follow.

dseq2 rna-seq • 4.1k views
ADD COMMENTlink modified 2.3 years ago • written 2.3 years ago by Elizabeth Sam0

Typical RNA-seq data analysis is as follows:

Align the fastq reads onto to Genome(using Hisat2) or Transcriptome(using bowtie2) which will give you output in sam/bam format.

Extract the raw counts for every transcript/gene in every replicate from every sample

Later this count data must provided into DEseq2 for further down stream analysis(see the manual)

If you are totally new to RNA-seq analysis, then kinldy make use of Trinity

ADD REPLYlink written 2.3 years ago by EVR510
2

The work flow OP wants to use is fine, no need for trinity.

ADD REPLYlink modified 2.3 years ago • written 2.3 years ago by WouterDeCoster37k
0
gravatar for WouterDeCoster
2.3 years ago by
Belgium
WouterDeCoster37k wrote:

If you (in R) run the following you can easily transfer the count data to deseq2:

counts <- featureCounts(bams, blablabla_restofcommand)$counts
deseqdata <- DESeqDataSetFromMatrix(countData=counts, colData=sampleInfo, design=~condition)

You obviously need to fill in some more parts or adjust colData and design to your experiment.

ADD COMMENTlink written 2.3 years ago by WouterDeCoster37k

Thanks a lot WouterDeCoster.

ADD REPLYlink written 2.3 years ago by Elizabeth Sam0

I have one more doubt. I have two text files as output from featureCounts(), one of normal and other tumor. To find the differentially expressed genes, I plan to merge the two files into one count file and give this as input to DESeq2. Is this approach correct?

ADD REPLYlink written 2.3 years ago by Elizabeth Sam0
1

Not appropriate to merge tumor and normal data. You can use this info as a covarite in the design.

ADD REPLYlink written 2.3 years ago by ShirleyDai40

Did I understood correctly that you are looking for differentially expressed genes between the normal and tumor samples?

ADD REPLYlink written 2.3 years ago by WouterDeCoster37k

Sorry for the late reply. Yes, I want to find the differentially expressed genes between normal and tumor samples.

ADD REPLYlink written 2.3 years ago by Elizabeth Sam0

As in my code example above, the counts object will hold all counts generated from the files in the bams object. So it's perfectly fine to have both the normal and tumor samples in there together. You are not merging the data, you are putting it together in one dataframe/object. The comment of ShirleyDai wasn't accurate.

ADD REPLYlink written 2.3 years ago by WouterDeCoster37k

Thanks WouterDeCoster.

ADD REPLYlink written 2.3 years ago by Elizabeth Sam0

What I mean taking tumor/normal info as a covariate is to make a multi-factorial model. Source: https://support.bioconductor.org/p/58893/

ADD REPLYlink written 2.3 years ago by ShirleyDai40

Right, that would be design in case of a paired analysis.

ADD REPLYlink written 2.3 years ago by WouterDeCoster37k
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