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Note: the “mature_counts.csv” needs to be transposed prior running the statistical analysis. This can be done either user the R script or using a script called “transpose_csv.py”.
Let’s initially create a “DESeq2” folder and copy the files needed for the statistical analysis:
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python transpose_csv.py --input mature_counts.csv --out mature_counts.txt |
Differential expression analysis using RStudio
Differential expression analysis for smRNA-Seq is similar to regular RNA-Seq. Since you have already done the step-wise analysis in session 5, in this session we will streamline the analysis by running a single R script.
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a. Open Windows Explorer.
b. Go to: H:\workshop\small_RNAseq
c. Create a new folder here called ‘DESeq2’ (NOTE: R is case-sensitive, so it must be named exactly like this)
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c. Hit the save button and save this file in the working directory you created above (H:\workshop\small_RNAseq\DESeq2). Name the R script ‘DESeq2.R’.
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Using R Studio, create a Text File and paste in the contents of this script.
Save it as launch_R.pbs in H:\workshop\small_RNAseq\DESeq2 (Same folder as DESeq2.R (Remember, H: is pointed at your HPC Home Folder.
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