ilyada/NestedWGCNA: v1.0.0 NestedWGCNA: analysis pipeline

NestedWGCNA v1.0.0 Initial release accompanying the manuscript submission. What's included utils.py — core algorithm: r²-based adjacency, √(1−r²) dissimilarity, UMAP+HDBSCAN clustering, core decomposition GenFocus.py — core-based expression normalization for FGM discovery NestedWGCNA_analysis.ipynb — full pipeline demonstrated on TCGA BLCA data, covering CGM/FGM discovery, enrichment analysis and TF validation data/ — TCGA BLCA expression matrix, cell-type signature files, and precomputed IMvigor210 module assignments (WGCNA baseline, NestedWGCNA CGMs, CGM cores, and immune FGMs) TF-based validation Sections 8–9 of the notebook implement transcription factor enrichment analysis (ChEA_2022 via Enrichr) and the TF knowledge score, a quantitative measure of TF co-regulation enrichment within modules, used as the primary biological validation in the manuscript. Notes IMvigor210 raw expression data requires controlled access via EGA (NCT02108652); precomputed cluster assignments are provided in data/ Requires Python ≥ 3.9; see environment.yml for dependencies