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ishworthapa authored Aug 25, 2019
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* R (packages: edgeR)
* Python (packages: pandas)

## Running step wise
## 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 step0_preprocess.R
```
### Step1 Calculate Stats
#### Based on the source of input file, enter RNASeq or microarray as input argument.
In this step, the count table is normalized and the covariance, standard deviation, mean and MFC are being calculated.
```
Rscript step1_calculateStats.R RNASeq
Rscript step1_calculateStats.R microarray
```
### Step2 Find Reference set
#### Based on the source of input file, enter RNASeq or microarray as input argument.

In this step, reference set of genes are being determined. The output file 'ref_cpm.csv' stores expression level of these reference genes.
```
Rscript step2_findRef.R RNASeq
Rscript step2_findRef.R microarray
```
### Step3 Find overlap using Fisher's Exact test
#### Enter number of samples in first group as first input argument.
#### Based on the source of input file, enter RNASeq or microarray as second input argument.

Since the example dataset has 115 samples in group1.
```
Rscript step3_overlapFisher.R 115 RNASeq
Rscript step3_overlapFisher.R 115 microarray
```

## 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 for RNASeq data.
Rscript step0_preprocess_microarray.R
Rscript step1_calculateStats.R microarray
Rscript step2_findRef.R microarray
Rscript step3_overlapFisher.R 115 microarray

## Running all the steps together
```
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```
Rscript DDR_Ref.R PYTHON_PATH
```

## 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

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