DE Visualization
##4-iii. CummeRbund R Analysis
The output of cuffdiff can be directly loaded into the R/BioConductor package to produce a sophisticated set of analysis results and visualizations.
Navigate to the correct directory and then launch R:
cd $RNA_HOME/de/tophat_cufflinks/ref_only/
R
A separate R tutorial file has been provided in the github repo for part 2 of the tutorial: Tutorial_Module4_Part2_CummeRbund.R. Run the R commands detailed in the R script. All results are directed to pdf file(s). The output pdf files can be viewed in your browser at the following urls. Note, you must replace cshl## with your own amazon instance number (e.g., "cshl01")).
- http://cshl##.dyndns.org/workspace/rnaseq/de/tophat_cufflinks/ref_only/Tutorial_Part2_cummeRbund_output.pdf
- http://cshl##.dyndns.org/workspace/rnaseq/de/tophat_cufflinks/ref_only/Tutorial_Part2_cummeRbund_output_extras.pdf
##SUPPLEMENTARY R ANALYSIS
Occasionally you may wish to reformat and work with cuffdiff output in R manually. Therefore we provide an optional/advanced tutorial on how to format your results for R and perform "old school" (non-cummeRbund analysis) on your data.
Expression and differential expression files will be read into R. The R analysis will make use of the transcript-level expression and differential expression files from cuffdiff. Copy the necessary files to a new directory.
cd $RNA_HOME/
mkdir -p final_results/tophat_cufflinks/ref_only
cd $RNA_HOME/final_results/tophat_cufflinks/ref_only
cp $RNA_HOME/de/tophat_cufflinks/ref_only/isoform* .
cp $RNA_HOME/de/tophat_cufflinks/ref_only/read_groups.info .
A separate R file has been provided in the github repo for part 3 of the tutorial: Tutorial_Module4_Part3_Supplementary_R.R
In this tutorial you will:
- Learn basic R usage and commands (common plots, and data manipulation tasks)
- Examine the expression estimates
- Create an MDS plot to visualize the differences between/among replicates, library prep methods and UHR versus HBR
- Examine the differential expression estimates
- Visualize the expression estimates and highlight those genes that appear to be differentially expressed
- Generate a list of the top differentially expressed genes
- Ask how reproducible technical replicates are.
Navigate to the correct directory and then launch R:
cd $RNA_HOME/final_results/tophat_cufflinks/ref_only/
R
Then, run the R commands detailed in the R script above. The output file can be viewed in your browser at the following url. Note, you must replace cshl## with your own amazon instance number (e.g., "cshl01")).
##ERCC DE Analysis This section will demonstrate the DE between the ERCC spike-in:
cd $RNA_HOME/de/tophat_cufflinks/ref_only
wget https://xfer.genome.wustl.edu/gxfer1/project/gms/testdata/bams/brain_vs_uhr_w_ercc/ERCC/Tutorial_Module4_ERCC_DE.R
chmod +x Tutorial_Module4_ERCC_DE.R
./Tutorial_Module4_ERCC_DE.R $RNA_HOME/refs/ERCC/ERCC_Controls_Analysis.txt $RNA_HOME/de/tophat_cufflinks/ref_only/gene_exp.diff
View the results here:
##edgeR Analysis A separate R tutorial file has been provided in the github repo for part 4 of the tutorial: Tutorial_Module4_Part4_edgeR.R
In this tutorial you will:
- Make use of the raw counts you generate above using htseq-count
- edgeR is a bioconductor package designed specifically for differential expression of count-based RNA-seq data
- This is an alternative to using cufflinks/cuffmerge/cuffdiff to find differentially expressed genes
First, create a directory for results:
cd $RNA_HOME/
mkdir -p de/tophat_counts
cd de/tophat_counts
cp $RNA_HOME/de/tophat_cufflinks/ref_only/gene* .
Create a mapping file to go from ENSG IDs (which htseq-count output) to Symbols:
cd $RNA_HOME/refs/hg19/genes
perl -ne 'if ($_=~/gene_id\s\"(ENSG\d+)\"\;\sgene_name\s\"(\S+)\"\;/){print "$1\t$2\n";}' genes_chr22_ERCC92.gtf | sort | uniq > ENSG_ID2Name.txt
cd $RNA_HOME/de/tophat_counts
R
Once you have run the edgeR tutorial, compare the sigDE genes to those saved earlier from cuffdiff:
cat $RNA_HOME/de/tophat_cufflinks/ref_only/DE_genes.txt
cat $RNA_HOME/de/tophat_counts/DE_genes.txt
Finally, pull out the gene symbols and visualize overlap with a venn diagram. This can be done with simple web tools like:
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