Set working directory

# Set working directory
setwd("/Volumes/bioinfomatics$/jurtasun/Courses/CBW2022/LMS_RNASeq/course/exercises")
getwd()
## [1] "/Volumes/bioinfomatics$/jurtasun/Courses/CBW2022/LMS_RNASeq/course/exercises"

  1. Read in count data and sample description. Identify how many factors are involved in this this experiment.

  2. Create the col_data from the sample description and check dimensions with the count matrix

  3. Construct DESeqDataSet object using count data and sample description.

    1. Apply Normalization with DESeq2 on the dds object just created

    2. Find the number of genes that are changed in knockdown samples versus control (i.e. KOa vs FFa and KOb vs FFa), at FDR 0.05

    3. Find the number of genes that are changed in the above situation with fold change threshold, i.e. fold change ratio > 2

  5. Draw MA plot and highlight all significant genes with adjusted p-value less than 0.05

    1. Use plotCounts() function to plot the counts for gene 497097

    2. Plot the un-normalized counts for the same gene