I'm going to digest and analyze the RNA-Seq transcriptomic data in the following link (GEO accession: GSE96058). I did many searches on the web, but I couldn't find an applicable strategy to analyze this data. So It would be very much appreciated if you could please help me out about how to start analyzing this type of data.
It is indicated that the data that is made available by the authors comprises log2 FPKM expression units:
Gene expression data in FPKM were generated using cufflinks 2.2.1
(default parameters except –GTF, --frag-bias-correct GRCh38.fa,
--multi-read-correct, --library-type fr-firststrand, --total-hits-norm, --max-bundle-frags 10000000). The resulting data was was post-processed by collapsing on 30,865 unique gene symbols
(sum of FPKM values of each matching transcript), adding to each
expression measurement 0.1 FPKM, and performing a log2 transformation.
In this case, one is very much limited by what one can do. It is undesirable to start any differential expression analysis with just FPKM units or their log2-transformed equivalents. I may suggest following the limma-trend pipeline, though, taking advice from this post by my colleague, keeping in mind that a pseudocount of 0.1 has already been added by the authors of the data in question: A: Differential expression of RNA-seq data using limma and voom()