No differentially expressed genes after multiple testing correction in mice

No differentially expressed genes after multiple testing correction in mice

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Hi all,

I am working with the RNA-seq data on mice (group A N=3 vs group B N=3). Mice are littermates, of which group A overexpresses a human transgene which I verified.

I have had .cram files from mouse data, and I converted them to .fastq files. Then I aligned them by hisat2 and used HTSeq count to generate the count files.

Here is an example of the commands:

#converting .cram files to fastq files
ls *.cram | parallel -j 8 --bar  'samtools view -h -T genome_mm10.fa {} |samtools collate -u -O  - | samtools fastq -1 {.}_paired1.fq.gz -2 {.}_paired2.fq.gz -0 /dev/null -s /dev/null -'

#trimming fastq files
ls *_paired1.fq.gz | parallel --bar -j12 'R2=$(echo {} | sed s/_paired1/_paired2/) && out=$(echo {} | sed s/_paired1.fq.gz/.fq.gz/) &&  trimmomatic PE {} $_paired2 -baseout $out LEADING:3 TRAILING:3 MINLEN:30'

#aligning and sorting sam files and converting them to bam files
ls *_1P.fastq.gz | parallel --bar -j8 'R2=$(echo {} | sed s/_1P.fastq.gz/_2P.fastq.gz/) && out=$(echo {} | sed s/_1P.fastq.gz/.bam/) && rg_sm=$(echo {} | cut -d. -f1) && hisat2 -1 {} -2 $R2 --dta -x /ref/mm10_indexed -p 4 --rg SM:$rg_sm --rg ID:**  --rg PL:RNAseq --rg LB:Illumina 2> ${out}.log | samtools sort -T /tmp/ -o $out 2>> ${out}.log'

#making the index file for bam files
ls *.bam| parallel --bar -j8 'samtools index {}'

#HTSeq count
ls *.bam| parallel --bar -j8 'out=$(echo {} | sed s/.bam/.count/) && htseq-count -f bam -r pos -q {} mm10.gtf.gz > $out'

Then I used DESeq2 to find differentially expressed genes.

dds <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,
                                  directory=folder,
                                  design=~condition)

But nothing was significant after multiple testing corrections.

While the other analysis by Limma and edgeR showed a few significant genes.
The output results of Limma and edgeR have some genes in common, while there is nothing in common between the results of DESeq2 compared to the limma and edgeR. Is it normal?

Another question is that when the padj value is not significant can we report the differentially expressed genes based on the p-value?


edgeR


DESeq2


Limma


mouse


RNA-seq

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