DOI: 10.5281/zenodo.17419882
title: Regulatory Network Analysis of SLE-Associated Regulon Activity using SCENIC pipeline

description: Aim: Find differentially activated regulons in SLE vs Controls in bulk-RNA seq data

This code was adapted from the pipeline described in the article: “Identification of regulons modulating the transcriptional response to SARS-CoV-2 infection in humans” by Padilla-Gálvez et al., 2024. Script: pyscenic_multiruns_batchcorr-sep.rmd

Source files:



toRun/counts/vsd_all.RData: R object containing variance-stabilized expression data, typically generated from DESeq2. Used for downstream visualization and differential expression analysis.

toRun/metadata/named-transcripts-info_v2.RData: Annotated transcript metadata with columns:



ID: Transcript identifier (Ensembl IDs)

gene_name: Associated gene symbol

transcript_biotype: Functional category (e.g. protein-coding, lncRNA, pseudogene)



toRun/counts/reduced_counts.csv:  Filtered expression matrix containing variance-stabilized counts for unique genes across all samples.


1. Run SCENIC pipeline

Input files: 



Config file: toRun/sle_scenic.config

Input data: toRun/counts/reduced_counts.loom

Resources folder: toRun/resources


Output file: 



resultsRun/output/multi_runs_looms/multi_runs_regulons_auc_trk.loom: SCENIC output loom   + RegulonsAUC: AUC scores for regulon activity across cells or samples  + CellID: Unique identifiers for each sample  + Regulons: Binary matrix of transcription factor–target relationships  + Gene: Gene names associated with each regulon

resultsRun/output/multi_runs_aucell/multi_runs_regulons_auc_trk.tsv: Tabulated AUC scores



RegulonsAUC: AUC scores for regulon activity across cells or samples

CellID: Unique identifiers for each sample

RegulonName: Transcription factor names associated with each regulon



resultsRun/output/multi_runs_cistarget/multi_runs_features_trk.csv.gz: Regulatory feature scores



Gene-motif associations: Motif enrichment scores for candidate target genes

Rankings: CisTarget ranking of regulatory evidence per gene

Motif: Motif ID and annotation used to infer regulation

Format: Compressed CSV for downstream analysis or validation



resultsRun/output/multi_runs_cistarget/multi_runs_regulons_trk.pkl.gz: Regulon object (Python pickle)



Regulons: Dictionary of transcription factors and their predicted target genes

Metadata: Includes motif support and confidence scores

Format: Compressed Python object for reuse in SCENIC or custom workflows



resultsRun/output/multi_runs_regulons_trk: 



Folder with regulon-level outputs

GeneLists: Individual files listing target genes per regulon

MotifEnrichment: Motif-level evidence supporting TF–target relationships

Intermediate files: Support reproducibility and allow inspection of regulon construction steps




2. Get differentiated regulons

Script:



resultsRun/scripts/dif_regulons.py: This script identifies differentially active transcriptional regulons between two groups of samples—typically SLE vs control—using SCENIC output.


Input file:



resultsRun/output/multi_runs_looms/multi_runs_regulons_auc_trk.loom: SCENIC output loom

resultsRun/metadata/metadata.csv: Sample-level metadata file. Must include:



sample_ID: Unique identifier matching CellID in the loom file

Group: Experimental condition label (e.g. "SLE" or "Ctrl")




Output files:



resultsRun/results/AUC_mtx.csv: Raw AUC matrix for all cells/samples and regulons

resultsRun/results/tf_targets.csv: Binary matrix of transcription factor targets per regulon

resultsRun/results/histogram_SLE_Ctrl_regulons.png: Histogram of adjusted p-values for differential regulons

resultsRun/results/difregs_SLE_Ctrl.csv: Final merged results: regulon name, p-values, adjusted p-values, log2FC