https://github.com/baderlab/pnc_pathwayanalysis

Pathway analysis pipeline for PNC data (GWAS + GSEA)
https://github.com/baderlab/pnc_pathwayanalysis

Last synced: about 5 hours ago
JSON representation

Pathway analysis pipeline for PNC data (GWAS + GSEA)

• Host: GitHub
• URL: https://github.com/baderlab/pnc_pathwayanalysis
• Owner: BaderLab
• Created: 2018-11-15T02:21:43.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-11-15T02:28:28.000Z (about 6 years ago)
• Last Synced: 2024-11-05T10:26:06.780Z (about 2 months ago)
• Language: Python
• Size: 36 MB
• Stars: 1
• Watchers: 13
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
* Instructions on how to run the Pathway Analysis pipeline for PNC data

* Nov 9, 2018

* By Shirley Hui

A) PERMUTE PHENOTYPES

1) Permute phenotypes

File: ROOT_DIR/permutePhenos.R

- This file permutes phenotype status in the phenoFile (see code). 

- Subjects with status -9 are not included in the permutation

Run: 

> Rscript perumtePhenos.R 

 * Note: Set variables inside script before running

Output: 

 * Each permutation is written out to a file with the same format as phenoFile

 * Note: pemutations are written to files to ensure reproducibility 

B) PERFORM GWAS (genome-wide associations)

1) Make gwas batch and submit scripts

File: ROOT_DIR/make_plink_assoc_bin_jobfiles.sh

- This script make a txt file that contains the call to run gwas (logistic regression + PC covariates) to compute snp-phenotype associations using plink.

Run:

> ./make_plink_assoc_bin_jobfiles.sh  0  

Example: ./make_plink_assoc_bin_jobfiles.sh volt_svt 0 100 1-5

Output:

 * a file called ROOT_DIR/jobfiles/plink_assoc__0--covar-.txt that contains the call to run one permutation of gwas (one call per line)

 * a file called ROOT_DIR/run_gwas_bin__0--covar-.sh that calls the txt file above via parallel

2) Compute gene-phenotype associations 

- Run ROOT_DIR/run_gwas_bin__0--covar-.sh script from previous

- The script runs GWAS (runGWAS.R) 0 to  times for pheno of interest using logistic regression and 0-n PC covariates supplied by the user 

Output:

 * pvalues for each SNP outputted to a file for each permuation (0 to )

C) PERFORM PATHWAY GSEA

The following assumes the pipeline is running on a high 

1) Make gsea batch and submit scripts

File: ROOT_DIR/code/src/MakeJobs.java

- This script will make a bunch of .sh files that contain the 8 parallel jobs that will run when it is called.

- If num perm = 100, 13 .sh files will be made.  If num perm= 1000, 125 .sh files will be made.  Note code doesn't really support num perms not equal to 100 or 1000.

- If you make any code changes, to compile script, run the ./compile.sh at the prompt

Run: 

> module load java

> java -cp ../out MakeJobs   bin 

Example: java -cp ../out MakeJobs volt_svt 100 bin 1-5

Output:

  * a bunch of job files located in ROOT_DIR/job_scripts/gsea/run-gsea---bin-covars.sh

  * a master submit script located in ROOT_DIR/submit-gsea--bin-covars.sh

2) Run gsea 

- Run submit script from previous

- You will get a bunch of warnings (which is ok) about how the submitted job time is the minimum of 15 mins.

- This will create several directories (one for each permutation).  In each directory there will be two output files (*gsea.txt - gsea output, *genes.txt - detailed output)

3) Move gsea output files into one directory

File: ROOT_DIR/moveFiles.sh

- This will move all output files into ROOT_DIR/gsea/bin/gsea-_covars where:

  * gsea.txt files get moved to obs directory,for perm0 or perm directory, for all other permutations

  * genes.txt files get moved to genes directory for all perms

Run: 

> moveFiles.sh  bin  

Example: moveFiles.sh volt_svt bin 1-5 100

- Delete empty directories and run txt and log files:

> rm -r Gsea-_bin-covars_*

> rm run_gsea_--bin-covars*

4) Normalize ES scores

File: ROOT_DIR/mygseaCalcNES.py

- This script will normalize the enrichment scores from step 2 above.

Run: 

> module load python/2.7.14-anaconda5.1.0

> python mygseaCalcNES.py  bin     

  

Example: python mygseaCalcNES.py volt_svt bin 1-5 100

Output:

- This will create a bunch of nes*.txt in the ROOT_DIR/nes/ directory

5) Compute FDR score

File: ROOT_DIR/compareFDR.R

- This script will compute FDR scores using NES from step 4)

Run: 

> module load r/3.4.3-anaconda5.1.0

> Rscript computeFDR.R  bin  

  

Example: Rscript computeFDR.R volt_svt bin 1-5 8

Output: - This will create a file fdr--bin-covars-.txt in the ROOT_DIR/fdr directory