bio-gene-regulatory-networks-multiomics-grn

What this skill does

## Version Compatibility

Reference examples tested with: SCENIC+ (current Snakemake workflow), pycisTopic 2.0+, pycistarget 1.0+, scanpy 1.10+, MACS3 3.0+; FigR/Signac/ArchR (R).

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.

SCENIC+ has undergone major API churn: the manual-object API (cisTopicObject, separate pycistarget, SCENICPLUS objects) is superseded by a Snakemake workflow (`scenicplus init_snakemake`). Pre-2024 tutorials are stale; verify against scenicplus.readthedocs.io before coding.

# 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-region -> target-gene triplets that link TF motif occupancy in accessible chromatin to gene expression.
- Python: SCENIC+ Snakemake pipeline (pycisTopic -> pycistarget -> eRegulons)
- Python: CellOracle base GRN (motif scan in Cicero co-accessible regions); R: Pando / FigR

## The Single Most Important Modern Insight -- Accessibility Defines the Candidate Enhancers, but Every Inference Arrow Leaks

The advance of multiomic GRN inference over expression-only methods is **where the candidate regulatory regions come from**: expression-only tools can only search promoter-proximal motifs, while scATAC nominates the actual distal enhancers active in these cells -- and most cell-type-specific regulatory information is distal. So an eGRN edge is a **triplet (TF -> region -> gene)**, with the region as the mechanistic anchor available for validation (ChIP/CUT&Tag, CRISPRi, reporter). But the reasoning chain leaks at every step: motif-present does not mean the TF binds (motifs are short, degenerate, and shared across a family -- the model cannot tell GATA1 from GATA2); accessible does not mean this TF holds the peak open; and peak-gene correlation does not mean the peak controls the gene. Treat an eGRN as a **prioritized hypothesis list**, not a wiring diagram.

The hardest and least-appreciated step is **peak-to-gene linking, which is confounded by cell-type composition**: across a heterogeneous dataset, any peak open in a cell type correlates with any gene expressed in that same type, whether or not the peak regulates it -- cell identity is a massive shared latent factor. Genome-wide peak-gene correlation therefore mostly recovers co-marker pairs. Mitigations (distance window, within-cell-type or GC/accessibility-matched null, requiring a motif, requiring the TF to be co-expressed) reduce but never eliminate it. Metacell aggregation, needed to beat scATAC sparsity, then introduces pseudo-replication: metacell-derived p-values are not calibrated significance and should be treated as ranking scores.

## eGRN Method Taxonomy

| Method | Citation | Approach | Data regime | Note |
|--------|----------|----------|-------------|------|
| SCENIC+ | Bravo Gonzalez-Blas 2023 *Nat Methods* | topics -> motif enrichment -> region-to-gene & TF-to-gene GBM -> eRegulons | paired or separate | reference eGRN method; heavy (Snakemake, cluster job) |
| CellOracle base GRN | Kamimoto 2023 *Nature* | motif scan in Cicero co-accessible regions; prebuilt base GRNs | scRNA alone OK | base GRN is a prior; feeds perturbation-simulation |
| Pando | Fleck 2023 *Nature* | regression with TF x peak-accessibility interaction term | paired (Seurat) | regions = peaks intersect conserved/annotated CREs |
| FigR | Kartha 2022 *Cell Genomics* | DORCs (genes with many correlated peaks) -> TF-DORC scores | SHARE-seq/paired | `pairCells` for unpaired (pairing caps quality) |
| DIRECT-NET | Zhang 2022 *Sci Adv* | XGBoost CRE-gene importance -> TF via motif | paired or scATAC alone | |
| TRIPOD | Jiang 2022 *Cell Syst* | nonparametric TF-peak-gene trio test, matched/conditional | paired | strong false-positive control |
| scMEGA | Li 2023 *Bioinform Adv* | integrate -> trajectory -> TF-gene network | paired, trajectory | lighter-weight |
| GLUE | Cao & Gao 2022 *Nat Biotechnol* | graph-linked latent embedding | **unpaired/diagonal** | run first to integrate, then a paired method |

## Decision Tree by Scenario

| Scenario | Recommended | Why |
|----------|-------------|-----|
| Paired 10x Multiome / SHARE-seq, want full eGRN | SCENIC+ | reference method; eRegulon triplets + activity |
| Paired data, lighter/faster | Pando or DIRECT-NET | single-regression / XGBoost, less infrastructure |
| Rigorous false-positive control on trios | TRIPOD | conditional/matched testing removes the composition confound |
| scRNA-seq only (no ATAC) | -> CellOracle prebuilt base GRN | base GRN is a prior; no paired data needed |
| Unpaired scRNA + scATAC (separate experiments) | GLUE to integrate first, then FigR/SCENIC+ | computed pairing caps all downstream link confidence |
| RNA-only regulons, no enhancers needed | -> scenic-regulons | promoter-proximal motif pruning suffices |
| Goal is in silico TF perturbation | -> perturbation-simulation | CellOracle/Dynamo simulate; build the base GRN here |

## SCENIC+ Pipeline (current Snakemake workflow)

**Goal:** Assemble eRegulons (TF -> enhancer -> gene) from paired or separate scRNA + scATAC.

**Approach:** Topic-model the ATAC with pycisTopic, call consensus peaks from per-cell-type pseudobulk (so cell-type labels are needed before peak calling), run pycistarget motif enrichment, then region-to-gene and TF-to-gene GBM regression to build triplets; orchestrate with Snakemake.

```python
# Initialize and run the Snakemake workflow (the supported modern entry point).
# init_snakemake scaffolds a config.yaml pointing at the scATAC fragments, the scRNA
# AnnData, the motif/cisTarget databases, and the cell-type annotation used for pseudobulk.
# scenicplus init_snakemake --out_dir scenicplus_run
# (edit scenicplus_run/Snakemake/config/config.yaml, then run from inside that dir:)
# cd scenicplus_run/Snakemake && snakemake --cores 16

# Region-to-gene search space defaults to min 1kb / max 150kb from the gene, capped at the
# nearest neighboring gene's promoter -- narrower than the +/-500kb used by ArchR/Signac.
```

```python
# Inspect the resulting eRegulons (TF -> region -> gene triplets). The output filename and
# directory are set in config.yaml (output_data); the direct (high-confidence) and extended
# (motif-similarity-inferred) tables are written there. The exact spelling has varied across
# versions, so resolve it by glob rather than hard-coding.
import glob, pandas as pd
ereg_file = glob.glob('scenicplus_run/**/eRegulon*direct*.tsv', recursive=True)[0]
eregulons = pd.read_csv(ereg_file, sep='\t')
summary = (eregulons.groupby('TF')
           .agg(n_regions=('Region', 'nunique'), n_genes=('Gene', 'nunique'))
           .sort_values('n_genes', ascending=False))
# Direct vs extended is a motif-to-TF annotation CONFIDENCE distinction, not topology:
# direct = curated/orthology; _extended adds motif-similarity-inferred (larger, noisier).
```

## CellOracle Base GRN (works without paired multiome)

**Goal:** Build a base GRN -- the candidate TF -> gene scaffold -- from accessibility, as a prior for context-specific modeling.

**Approach:** Define active regions by Cicero co-accessibility (in R), scan them for TF motifs, and format the result as the base GRN; or load a prebuilt base GRN and skip ATAC entirely.

```python
import celloracle as co
import pandas as pd

# Custom base GRN: peaks already filtered to Cicero co-accessible, promoter-linked regions.
peaks = pd.read_parquet('processed_peak_file.parquet')   # columns: peak_id, gene_short_name
tfi = co.motif_analysis.TFinfo(peak_data_frame=peaks, ref_genome='hg38')
tfi.scan(fpr=0.02)