How bigaspinosalab/resources_crc_metacohort_generation works
This repository documents an R-script workflow for assembling a colorectal cancer metacohort from seven GEO studies. The clinical-data branch retrieves patient phenotypes, uses shared functions, and removes records lacking DFS information and duplicate samples. A separate expression branch downloads CEL files, normalizes expression data, and removes batch effects. The documented result combines phenotype and expression data for 1,118 patients. Source excerpts were unavailable, so the graph follows README-documented responsibilities and omits unverified implementation details and connections between branches.
Main entry points: complete_metacohort.R Metacohort completion · clinical_data_extraction.R Clinical extraction · clinical_data_extraction_misc.R Clinical helpers
Components 10
Clinical data
- Clinical extraction
scripts/clinical_data_extraction.R - Clinical helpers
scripts/clinical_data_extraction_misc.R - DFS and duplicate filtering
scripts/clinical_data_extraction.R
Expression data
- CEL download
scripts/complete_metacohort.R - Expression normalization
scripts/complete_metacohort.R - Batch removal
scripts/complete_metacohort.R
Metacohort output
- Metacohort completion
scripts/complete_metacohort.R - Metacohort data
Other components
- Researcher
- GEO studies
Connections 12
- Researcher → Clinical extraction: runs
- Clinical extraction → GEO studies: retrieves phenotypes
- Clinical extraction → Clinical helpers: sources functions
- Clinical extraction → DFS and duplicate filtering: filters samples
- Researcher → Metacohort completion: runs
- Metacohort completion → GEO studies: downloads CEL files
- Metacohort completion → CEL download: performs
- Metacohort completion → Expression normalization: performs
- Metacohort completion → Batch removal: performs
- Metacohort completion → Metacohort data: produces
- DFS and duplicate filtering → Metacohort data: contributes phenotype data
- Batch removal → Metacohort data: contributes expression data