Regulatory Network Analysis of SLE-Associated Regulon Activity using SCENIC pipeline

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