bio-gene-regulatory-networks-multiomics-grn

name	bio-gene-regulatory-networks-multiomics-grn
description	Build enhancer-driven gene regulatory networks by integrating single-cell RNA-seq and ATAC-seq data using SCENIC+ to identify eRegulons linking transcription factors to enhancers and target genes. Use when analyzing 10x multiome or paired scRNA+scATAC data to infer cis-regulatory GRNs.
tool_type	python
primary_tool	SCENIC+

Multiomics GRN Inference

Build enhancer-driven gene regulatory networks from paired single-cell RNA-seq and ATAC-seq data. SCENIC+ extends SCENIC by linking TFs to their enhancers and target genes through eRegulons.

SCENIC+ Overview

Component	Tool	Purpose
Topic modeling	cisTopic	Identify cis-regulatory topics from scATAC
Region-to-gene	SCENIC+	Link accessible regions to target genes
TF-to-region	SCENIC+	Connect TFs to their enhancer targets
eRegulon assembly	SCENIC+	Combine into TF-enhancer-gene triplets

Input Preparation

From 10x Multiome (CellRanger ARC)

import scanpy as sc
import pycisTopic
from pycisTopic.cistopic_class import create_cistopic_object_from_fragments

# Load RNA from CellRanger ARC output
adata_rna = sc.read_10x_h5('filtered_feature_bc_matrix.h5', gex_only=True)
adata_rna.var_names_make_unique()
sc.pp.filter_cells(adata_rna, min_genes=200)
sc.pp.filter_genes(adata_rna, min_cells=3)

# Load ATAC fragments
fragments_file = 'atac_fragments.tsv.gz'

Call Peaks with MACS3

import subprocess

# Call peaks from fragments file for region universe
subprocess.run([
    'macs3', 'callpeak',
    '-t', 'atac_fragments.tsv.gz',
    '-f', 'BEDPE',
    '--nomodel', '--shift', '-75', '--extsize', '150',
    '-g', 'hs',
    '--keep-dup', 'all',
    '-n', 'multiome_peaks',
    '--outdir', 'macs3_output'
], check=True)

Create cisTopic Object

from pycisTopic.cistopic_class import create_cistopic_object_from_fragments
from pycisTopic.lda_models import run_cgs_models

# Create binary accessibility matrix from fragments
path_to_regions = 'macs3_output/multiome_peaks_peaks.narrowPeak'
cistopic_obj = create_cistopic_object_from_fragments(
    path_to_fragments=fragments_file,
    path_to_regions=path_to_regions,
    path_to_blacklist='hg38-blacklist.v2.bed',
    n_cpu=8
)

# Run LDA topic modeling
# Test multiple topic numbers; select by model metrics
models = run_cgs_models(cistopic_obj, n_topics=[10, 20, 30, 40, 50],
                        n_cpu=8, n_iter=300, random_state=42)

# Select best model (highest coherence, lowest perplexity)
from pycisTopic.lda_models import evaluate_models
model = evaluate_models(models, return_model=True)
cistopic_obj.add_LDA_model(model)

SCENIC+ Workflow

import scenicplus
from scenicplus.scenicplus_class import SCENICPLUS
from scenicplus.wrappers.run_scenicplus import run_scenicplus

# Prepare SCENIC+ object
scplus_obj = SCENICPLUS(
    adata_rna,           # scRNA-seq AnnData
    cistopic_obj,        # cisTopic object with topics
    menr=None            # will be computed
)

# Run complete SCENIC+ pipeline
# This performs: motif enrichment, region-to-gene linking, eRegulon assembly
run_scenicplus(
    scplus_obj,
    variable=['GeneExpressionLevel'],
    species='hsapiens',
    assembly='hg38',
    tf_file='allTFs_hg38.txt',
    save_path='scenicplus_output/',
    biomart_host='http://www.ensembl.org',
    upstream=[1000, 150000],    # search space upstream of TSS
    downstream=[1000, 150000],  # search space downstream of TSS
    calculate_TF_eGRN_correlation=True,
    calculate_DEGs_DARs=True,
    export_to_loom_file=True,
    export_to_UCSC_file=True,
    n_cpu=8
)

eRegulon Interpretation

import pandas as pd

# eRegulons link TFs -> enhancers -> target genes
eregulons = scplus_obj.uns['eRegulon_metadata']
print(f'Found {eregulons["Region_signature_name"].nunique()} eRegulons')

# Summarize eRegulons
ereg_summary = eregulons.groupby('TF').agg(
    n_regions=('Region', 'nunique'),
    n_genes=('Gene', 'nunique')
).sort_values('n_genes', ascending=False)
print(ereg_summary.head(20))

# Activating vs repressive eRegulons
# (+) suffix = activating (TF expression positively correlated with targets)
# (-) suffix = repressive (TF expression negatively correlated with targets)
activating = eregulons[eregulons['Region_signature_name'].str.contains(r'\(\+\)')]
repressive = eregulons[eregulons['Region_signature_name'].str.contains(r'\(-\)')]
print(f'Activating: {activating["TF"].nunique()}, Repressive: {repressive["TF"].nunique()}')

eRegulon Activity Scoring

from scenicplus.eregulon_enrichment import score_eRegulons

# Score eRegulon activity per cell (like AUCell but for eRegulons)
score_eRegulons(scplus_obj, ranking_key='eRegulon_AUC', enrichment_type='region')
score_eRegulons(scplus_obj, ranking_key='eRegulon_AUC_gene', enrichment_type='gene')

# eRegulon AUC matrix
ereg_auc = scplus_obj.uns['eRegulon_AUC']['Gene_based'].copy()

Visualization

import matplotlib.pyplot as plt
from scenicplus.plotting.dotplot import heatmap_dotplot

# eRegulon activity heatmap by cell type
heatmap_dotplot(
    scplus_obj,
    size_matrix=scplus_obj.uns['eRegulon_AUC']['Gene_based'],
    color_matrix=scplus_obj.uns['eRegulon_AUC']['Region_based'],
    group_variable='cell_type',
    save='eregulon_dotplot.pdf'
)

# Network visualization of top eRegulon
from scenicplus.plotting.grn_plot import plot_eRegulon

plot_eRegulon(scplus_obj, tf_name='PAX5', save='pax5_eregulon.pdf')

FigR Alternative

FigR infers TF-gene regulatory interactions from paired scRNA+scATAC without topic modeling.

library(FigR)
library(Seurat)
library(Signac)

# Load Seurat object with RNA and ATAC assays (from Signac)
seurat_obj <- readRDS('multiome_seurat.rds')

# Run FigR
# Step 1: compute peak-gene correlations
peak_gene_cors <- runGenePeakcorr(
    ATAC.se = seurat_obj@assays$ATAC,
    RNAmat = seurat_obj@assays$RNA@data,
    genome = 'hg38',
    nCores = 8,
    p.cut = 0.05  # significance threshold for peak-gene links
)

# Step 2: infer TF-gene regulation scores (DORC-based)
fig_results <- runFigR(
    ATAC.se = seurat_obj@assays$ATAC,
    dorcTab = peak_gene_cors,
    genome = 'hg38',
    dorcMat = getDORCScores(seurat_obj@assays$ATAC, peak_gene_cors
    rnaMat  seurat_objassaysRNAdata
    nCores  



top_links  fig_resultsorderfig_resultsScore decreasing   
headtop_links 

Resource Requirements

Dataset Size	RAM	Time	Notes
5K cells	16 GB	2-4 hours	Feasible on laptop
20K cells	64 GB	8-12 hours	Standard server
50K+ cells	128 GB+	24+ hours	Subsample or use HPC

Related Skills

• scenic-regulons - RNA-only regulon inference with pySCENIC
• single-cell/scatac-analysis - scATAC-seq preprocessing with Signac/ArchR
• atac-seq/atac-peak-calling - Peak calling for region universe
• chip-seq/motif-analysis - Motif enrichment for TF identification