I tried to estimate the DEGs for a set of transcriptome from the study. In the analysis pipeline, after adapter removal using Trim galore, I first mapped the reads to reference using Hisat2. Then estimated the counts using featureCounts. These raw counts were input to DESEq2 for DEGs estimation following the tutorial.
My problem here is only around 30-40% of top up or down-regulated DEGs (for ex, among top 600 genes) estimated in the study (whole DEGs list provided as suppl files) match with my estimation. Please note that GTF file and genome index files are same in both analysis.
So at what point the big difference occurs? Is there something wrong what I did in my pipeline? Am aware that Tophat is outdated and will it make such a big difference in estimation?
•
written
by
anc.informatics •
1 answer
Well, it's not necessarily Tophat, but cufflinks and DESeq2 are very different methodologies. I would not be overly worried if you don't capture 100% of their results. How does it look in the opposite direction (how many of your identified DEGs are in their list)? You can't compare apples to apples since you are using a very different (and better) method. You didn't necessarily do anything wrong.
Log in to answer this question.
-
Cell-type deconvolution before or after ComBat in methylation EWAS?
written by Eliveri •
-
Both up- and down-regulated gene sets return the same GO pathway analysis results?
written by xqyn •
-
Combining two closely related bacterial annotated genomes to analyze as one pooled group
written by brayas13 •
-
Outlier after DEseq2 in meta-analysis
written by a.stef.44 •
-
DESeq2 error
written by sooni •
-
Troubleshooting RNA-seq data with DNA contamination
written by DKA •
-
the dispersion estimation of edgeR and DESeq2
written by tommy •
-
Does comparison between two groups still make sense if PCA plot suggest no difference?
written by wangziwei0010 •
-
low counts in DESeq2
written by AlexStar •
-
Counts plot does not match fold change between groups
written by juara •
