https://github.com/gersteinlab/ngr
Network-based Gene Ranking of Contribution to Cancer [HM]
https://github.com/gersteinlab/ngr
Last synced: 3 months ago
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Network-based Gene Ranking of Contribution to Cancer [HM]
- Host: GitHub
- URL: https://github.com/gersteinlab/ngr
- Owner: gersteinlab
- Created: 2019-05-15T18:40:26.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-10-04T18:57:46.000Z (over 3 years ago)
- Last Synced: 2025-01-20T12:07:43.680Z (5 months ago)
- Language: Python
- Homepage:
- Size: 234 KB
- Stars: 0
- Watchers: 9
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.txt
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README
All code is on GitHub.
All data sufficient to run the code is on Farnam. In other words, Farnam is sufficient to reproduce all results.
More data/analyses might be found locally (e.g. gene_gene_matrix for more pathway databases than selected one (KEGG))
Feature extraction is done within each subfolder. Each subfolder has its code/ directory.
Each directory corresponds to a directory/data type. Scripts are to be run locally unless noted otherwise.
Pathway extraction and analysis:
To extract and analyze pathways: run extract_pathways.Rmd in pathways/code/
TCGA data and feature extraction:
In tcga/:
Clinical data in clinical_data/ are provided by Tao Qing of Pusztai lab; see clinical_data/REAMDE.txt if needed.
To generate differential expression information: run code/run_tcga-expr.sh locally. Move results and slurm log to code/results/ directory.
Note on differential expression analysis: currently, emphasis is put on FDR<=0.05 only; no logFC threshold. If logFC threshold is to be enforced, update and rerun code/tcga-diff_exp.R to use glmTreat() for model training: see section 2.12 in edgeR manual for more details.
Differential expression analysis results are in code/results/
For data download, annotation (using ANNOVAR), and feature extraction in somatic and germline TCGA variants, see tcga/README.txt
For PPI ID conversion, processing, and convesion to matrices, see ppi/README.txt
(Optional, ~ OBSOLETE as new PPIs have been generated and sample-level results are used) PPI metric (e.g. centrality) generation:
To generate betweenness centrality results, run (full commands in NGR Board sheet) ppi_centrality_script.sh in ppi/code
Betweenness centrality results are in ppi/code/results
For gold standard list generation, see gene_lists/README.txt.
Combined score generation:
To merge features and generate combined scores to be used as inputs to the method, locally run in base/:
Note: This R script is usually run on macbook but should execute successfuly on Farnam as needed if all R packages are installed.
module load R
Rscript merge_features.R -v somatic_MC3
Rscript merge_features.R -v germline
PPI network matrices:
To generate PPI matrices to be used by the Python script of the method, run sbatch convert_ppi_network_to_matrix_script.sh in ppi/code/
For result generation, see method/README.txt
For method comparison and figure generation (figures or data related to Circos), see analysis/README.txt