Regulatory Network Inference (SCENIC/REACTOR)
This page documents the SCENIC/REACTOR-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 — Earlier Draft Activity Scoring (AUCell) + Binarization => SCENIC essentially
- Input: Single-cell counts
- Purpose: reduce sparsity and improve robustness
Step 3 — REACTOR Donor-Cell-Type Activity Summaries
- Source update: Hasan reported a new REACTOR analysis on 2026-06-22.
- Input representation: binarized SCENIC regulons.
- Aggregation level: donor-cell_type combinations.
- Activity summary: for each donor-cell_type combination, REACTOR calculates the percentage of cells in which each regulon is active.
- Interpretation: this percentage represents the fraction of cells in the donor’s cell-type population with active regulon signal
Step 4 — Differential Testing
- Contrast: T2D vs Control.
- Within-population comparison: percentages of active cells are compared between conditions within each population.
- Test: ROTS.
Software
- pySCENIC v0.12.1 todo check out
- REACTOR
- ROTS