Perform DESeq2 analysis on RNASeq data

[laurahspencer]

Ran DESeq2 analysis on gene count matrix that Giles produced using STAR and all RNASeq data aligned to genome (not scrubbed of non-arthropoda reads). Here's my RMarkdown Notebook, RNASeq.html. A quick summary:

I removed quite a few low-frequency genes:

• No. genes before filtering: 43,800
• No. genes remaining after pre-filtering: 7,075
• No. of genes dropped: 36,725
• % of fragments remaining after pre-filtering: 95.551%

Below is a PCA of the two sign. principal components. A perMANOVA test indicates the treatments vary in multivariate space. There's also a heat map of the top 10% most variable genes (~700 genes). Differential expression analysis found a total of 40 DEGs. No significantly enriched biological processes. I suspect that improving our alignment will increase the no. of DEGs.

[laurahspencer]

Here is a CSV file with DEG stats and annotation info. Of the 40 DEGs 11 were annotated: DEGs.csv

Here's gene names for those that are annotated (from DAVID):

[laurahspencer]

NEW analysis approach:

• @ggoetznoaa align all reads (NOT megan-scrubbed) to "original" trinity de-novo transcriptome using ALL data (NOT megan-scrubbed reads)
• get @laurahspencer the gene count matrix
• @laurahspencer blast the "original" transcriptome to Uniprot database
• @laurahspencer run the gene count matrix through DEG analysis