I put a whole bunch of libraries through the same protocol as before - use Kallisto to create pseudoalignments for libraries, use a Trinity pipeline script to merge Kallisto counts into a matrix, analyze matrix with DESeq2, produce list of significantly different genes (padj <= 0.005)

At this point, I now have a list of significantly-different genes for each difference in condition

Next steps:

  • Create newline-separated text file containing significantly-different genes for each comparison. Is input for next step.
  • Use this script to get GO terms for significantly-different genes for each comparison.
  • Use GO assignments to perform enrichment analysis

As before, the comparisons are the following:

  • Day 0 Ambient vs. Day 17 Ambient (118, 132, 178 vs 463, 481, 485)
  • Day 0+2 Elevated vs Low (127/173/72/272/280/294 vs 151/254)
  • Day 0+2 Elevated vs Ambient (127/173/72/272/280/294 vs 118/132/178/334/349/359)
  • Day 0+2 Ambient vs Low (118/132/178/334/349/359 vs 151/254)

Alright, here’s a link to my results from the DESeq analyses. Again, the most important is the table of genes with significantly different expressions.

Here’s the script used to generate all results

  • Results, which contain the following:
  • Table of genes with significantly different expressions (adjusted pval <= 0.005), both with and without column headers
  • A variety of MA plots with the following conditions:
    • All results
    • All results, LFC estimates shrunk using apeglm
    • Results with a p-value <= 0.05
    • All results, with p-values <= 0.005 highlighted
  • Dispersion estimates