Dear all,
I have RNA-seq data (3 control vs 3 KD replicates) where I used STAR for mapping. After merging the bam files of control and KD samples separately (using samtools) I ran rMATS to detect differential alternative splicing events. I focused on the [AS_Event].MATS.JCEC.txt files and using a custom pipeline I only selected the significant genes based on FDR < 0.05.
To see how robust is rMATS I tried to swap control and KD samples (e,g, ctrl1,kd2,kd3 vs. kd1,ctrl2,ctrl3) and ran rMATS again. I again focused on the [AS_Event].MATS.JCEC.txt files and selected the significant genes based on FDR < 0.05.
The thing that I faced and do not understand is that I do get significant alternative splicing events even after swapping samples and the overlap with the approach where I did not swap the samples is a lot. For example, in the original approach, I got 266 significant SE events and after swapping I got 310 SE events and the overlap between these two is 120 which is a lot.
Could someone please help me to understand this? Thanks.
The rMATS code that I use:
rmats.py --b1 group1_merged_bam.txt --b2 group2_merged_bam.txt --gtf /gencode.vM25.annotation.gtf -t single --readLength 80 --nthread 4 --od /output --tmp /directory1
