Hi
After denovo assembly, the assembled transcripts were quantified using salmon it generated a quant.sf file
can I use this file as an input for differential gene expression analysis using edgeR ?
I had more than ~100 samples to analyze,
Can you provide some good tutorial easily understandable, very new to bioinformatics research.
Suggestions Please!
head quant.sf
Name Length EffectiveLength TPM NumReads
TRINITY_DN30552_c0_g1_i1 772 523.000 2.826146 133.000
TRINITY_DN30585_c0_g1_i1 572 323.000 0.756946 22.000
TRINITY_DN30563_c0_g1_i1 516 267.000 56.484784 1357.057
TRINITY_DN30563_c0_g2_i1 515 266.000 382.614124 9157.943
TRINITY_DN30577_c0_g1_i1 1130 881.000 1.337133 106.000
TRINITY_DN30527_c1_g1_i1 366 117.000 3.035056 31.953
TRINITY_DN30527_c0_g1_i1 446 197.000 4.343794 77.000
TRINITY_DN30562_c0_g1_i1 236 16.266 4.099455 6.000
TRINITY_DN30526_c0_g1_i1 384 135.000 1.399458 17.000
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sunnykevin97
The output of salmon are transcript level abundance estimates. These should be aggregated to the gene level (so gene counts, not transcript counts/abundances because edgeR and company perform gene level differential analysis) e.g. with the tximport package from Bioconductor and then analyzed with edgeR. Both tximport and edgeR have comprehensive manuals with a lot of example code, please read them extensively.
tximport seems to be very hard for me to understand, some simple example to understand.
Read https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html and linked references
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Have you searched online for "differential expression salmon"? That would have shown you this comment by genomax on a previous question that addresses your problem.