DDR

Requirements:

• R (packages: edgeR)
• Python (packages: pandas)

Figure 1. The Workflow of Data-Driven Reference (DDR) Package

RNASeq Pipeline:

Following series of steps show how to run the DDR method on RNASeq data.

Step0 Preprocess

Step0 should be used to format the count table such that the input table has the gene expression table with first n columns as group1 samples (Triple negative:TN) and remaining columns as samples from group2 (other:OT). Rows represent the ENSG ids.

Rscript src/step0_preprocess.R

Step1 Calculate Stats

In this step, the count table is normalized and the covariance, standard deviation, mean and MFC are being calculated.

Rscript src/step1_calculateStats.R RNASeq

Step2 Find Reference set

In this step, reference set of genes are being determined. The output file 'ref_cpm.csv' stores expression level of these reference genes.

Rscript src/step2_findRef.R RNASeq

Step3 Find overlap using Fisher's Exact test

Enter number of samples in first group as first input argument.

Since the example dataset has 115 samples in group1.

Rscript src/step3_overlapFisher.R 115 RNASeq

Output files

• final_out.csv: Normalized count data
• ref_cpm.csv: Expression of reference genes
• overlap_test_fdr_1_[RNASeq|microarray].csv or : Differentially expressed genes with fdr < 0.1
• overlap_test_fdr_05_[RNASeq|microarray].csv: Differentially expressed genes with fdr < 0.05

Pipeline for microarray data.

Note that the steps are similar to that for RNASeq data.

Rscript src/step0_preprocess_microarray.R
Rscript src/step1_calculateStats.R microarray 
Rscript src/step2_findRef.R microarray
Rscript src/step3_overlapFisher.R 115 microarray

Running all the steps together

Rscript DDR_Ref.R

If you have another python version that has pandas:

Rscript DDR_Ref.R PYTHON_PATH