Differential Expression

3-ii. Differential Expression

Use Ballgown to compare the tumor and normal conditions. Refer to the Ballgown manual for a more detailed explanation:

• https://www.bioconductor.org/packages/release/bioc/html/ballgown.html `

Change to ref-only directory:

mkdir -p $RNA_HOME/de/ballgown/ref_only/
cd $RNA_HOME/de/ballgown/ref_only/

Perform UHR vs. HBR comparison, using all replicates, for known (reference only mode) transcripts:

First create a file that lists our 6 expression files, then view that file, then start an R session where we will examine these results:

printf "\"ids\",\"type\",\"path\"\n\"UHR_Rep1\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep1\"\n\"UHR_Rep2\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep2\"\n\"UHR_Rep3\",\"UHR\",\"$RNA_HOME/expression/stringtie/ref_only/UHR_Rep3\"\n\"HBR_Rep1\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep1\"\n\"HBR_Rep2\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep2\"\n\"HBR_Rep3\",\"HBR\",\"$RNA_HOME/expression/stringtie/ref_only/HBR_Rep3\"\n" > UHR_vs_HBR.csv
cat UHR_vs_HBR.csv

R

A separate R tutorial file has been provided in the github repo for part 1 of the tutorial: Tutorial_Module4_Part1_ballgown.R. Run the R commands detailed in the R script.

Once you have completed the Ballgown analysis in R, exit the R session and continue with the steps below.

What does the raw output from Ballgown look like?

head UHR_vs_HBR_gene_results.tsv

How many genes are there on this chromosome?

grep -v feature UHR_vs_HBR_gene_results.tsv | wc -l

How many passed filter in UHR or HBR?

grep -v feature UHR_vs_HBR_gene_results_filtered.tsv | wc -l

How many differentially expressed genes were found on this chromosome (p-value < 0.05)?

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | wc -l

Display the top 20 DE genes. Look at some of those genes in IGV - do they make sense?

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | sort -rnk 3 | head -n 20 #Higher abundance in UHR
grep -v feature UHR_vs_HBR_gene_results_sig.tsv | sort -nk 3 | head -n 20 #Higher abundance in HBR

Save all genes with P<0.05 to a new file.

grep -v feature UHR_vs_HBR_gene_results_sig.tsv | cut -f 6 | sed 's/\"//g' > DE_genes.txt
head DE_genes.txt

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