Deseq2 Differential expression analysis

TASK

Deseq2 Differential expression analysis

We have done quantification at the gene level (with featureCounts), hence we have 12 output files for DeSeq2 that is differential expression analysis (with DESeq2).

Using twelve files representing output from featureCount use it to conduct differential analysis experiment. We are comparing two conditions read shuffled and read shuffled with errors. The control experiment is the main read that has not been shuffled or does not contain error. Basically we have sample_01_counts this corresponds to the control experiment for DeSeq1, we have sample_01_shuffled_counts and then we have Sample_01_shuffled_with errors_counts. So this three files will be used for the deseq2. This step will be repeated using sample 2, sample 3 and sample 4 for the same conditions. Report the result and the number of differentially expressed genes. Explain the result, significance of result. Draw adequate graphs. You can create your metadata file. A file made to track the file names and conditions (Figure 16). The types of conditions available are shuffled with errors (SE), control (ORG) and shuffled only (S). This will help show the genes that are expressed under different conditions.

Your metadata can be something like this.

Two files

Sampe of the read files

You can follow this link for assistance. In this file there is a guide on how to conduct DeSeq2 from Feature Count matrix.

https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#input-data.