Regulatory Network Inference (pySCENIC)
This page documents the pySCENIC-based regulon analysis pipeline. See Methods for the manuscript prose.
Pipeline
Step 1 — Regulon Inference (GRNBoost2)
- Input: raw-count scRNA-seq expression matrix
- Runs: 40 independent GRNBoost2 runs
- Pruning: motif-based using motifs-v9-nr.hgnc (m0.001)
- CisTarget database: hg38 RefSeq r80 ±10 kb TSS (
hg38__refseq-r80__10kb_up_and_down_tss.mc9nr) - Aggregation: regulons retained if recovered in ≥ 25% of runs
Step 2 — Activity Scoring (AUCell)
- Input: ComBat-seq–corrected pseudobulk profiles
- Purpose: reduce sparsity and improve robustness
Step 3 — Differential Testing
- Per cell type and ancestry: regulon activity compared between T2D and healthy
- Test: Wilcoxon rank-sum test
- Correction: FDR (Benjamini–Hochberg)
Software
- pySCENIC v0.12.0