Good morning everyone,
While I was doing some bibliography, I found the following article, Threshold-seq: a tool for determining the threshold in short RNA-seq datasets. (Bioinformatics. 2017 Jul 1;33(13):2034-2036. doi: 10.1093/bioinformatics/btx073.) https://www.ncbi.nlm.nih.gov/pubmed/28203700 which describe a tool that provide how many reads need to support a short RNA molecule in a given dataset before it can be considered different from ‘background.
My question is : can I use this tool to have a number of reads for each sample (lets say a int of 14 reads), pass to zero the numbers that have a number inferior to this int in my count matrix, and provide this count matrix to the DESeq2 functions for differential expression analysis ?
While I understand that DESeq2 expect as input un-normalized counts, my question is : is this kind of filtering affect the internal model of DESeq2 ? If so, may I ask how exactly ?
I have noticed the answer about filtering in other posts, like this one : https://support.bioconductor.org/p/65256/ but I do not really know how to translate them for my question. Especially, the script output a int for every sample, so I am actually quite puzzled about how I could apply this threshold number with Deseq2.