Dear All,
I would like to have critics/advices regarding how I plan to analyse my RNAseq result.
Basically, we have sequenced a transcriptome of a non model eukaryote (no genome sequence available).
Only reference we have is transcript contig from the same species coming from another RNAseq experiment.
The RNAseq was done in duplicates on 1 control sample and 2 samples with different conditions using illumina 50 nts single reads.
The bioinformatics analysis of the differential gene expression is the following:
- mapping versus the reference and get the table unique read count/transcript.
- then use edgeR to compute common.dispersion
- compute exactTest with fisher test to compare control vs Sample 1 and control vs sample 2.
- get the top differentially expressed with an p-value < 0.01.
I was wondering if this approach makes sense and if the statistical model is adapted to my experiment (mapping on transcript build on a denovo RNAseq).
Thank you in advance for your help.
Greg
I would like to have critics/advices regarding how I plan to analyse my RNAseq result.
Basically, we have sequenced a transcriptome of a non model eukaryote (no genome sequence available).
Only reference we have is transcript contig from the same species coming from another RNAseq experiment.
The RNAseq was done in duplicates on 1 control sample and 2 samples with different conditions using illumina 50 nts single reads.
The bioinformatics analysis of the differential gene expression is the following:
- mapping versus the reference and get the table unique read count/transcript.
- then use edgeR to compute common.dispersion
- compute exactTest with fisher test to compare control vs Sample 1 and control vs sample 2.
- get the top differentially expressed with an p-value < 0.01.
I was wondering if this approach makes sense and if the statistical model is adapted to my experiment (mapping on transcript build on a denovo RNAseq).
Thank you in advance for your help.
Greg
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