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+

Version Compatibility

Reference examples tested with: Cell Ranger 8.0+, MACS3 3.0+, matplotlib 3.8+, pandas 2.2+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Multiomics GRN Inference

"Build an enhancer-driven gene regulatory network from my multiome data" → Integrate scRNA-seq and scATAC-seq to identify eRegulons: transcription factor-enhancer-target gene triplets linking TF binding to chromatin accessibility and gene expression changes.

• Python: SCENIC+ pipeline with scenicplus for eRegulon assembly
• Python: pycisTopic for topic modeling of scATAC-seq regions

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

Goal: Assemble enhancer-driven gene regulatory networks (eRegulons) linking transcription factors to their target enhancers and downstream genes from paired scRNA+scATAC data.

Approach: Create a SCENICPLUS object from preprocessed scRNA-seq AnnData and cisTopic ATAC object, then run the complete pipeline which performs motif enrichment, region-to-gene linking, and eRegulon assembly.

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

Goal: Summarize the discovered eRegulons to identify the most active transcription factors and distinguish activating from repressive regulation.

Approach: Parse the eRegulon metadata table to count regions and target genes per TF, then separate activating (+) and repressive (-) eRegulons by their name suffix.

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_obj@assays$RNA@data,
    nCores = 8
)

# Top regulatory TF-gene links
top_links <- fig_results[order(abs(fig_results$Score), decreasing = TRUE), ]
head(top_links, 20)

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