Gene Regulatory Network Inference

GRN inference begins by constructing a co-expression network from single-cell transcriptomic data. The core assumption is that genes with correlated expression patterns across cells are likely to be co-regulated or functionally related.

• Mutual information and Spearman correlation are common metrics for quantifying pairwise gene-gene relationships
• Single-cell data introduces unique challenges including dropout events and zero-inflation that can distort correlation estimates
• Methods like SCENIC and GENIE3 use tree-based regression or random forests to capture non-linear dependencies beyond simple linear correlation
• The resulting co-expression network serves as the scaffold upon which regulatory directionality is later imposed

A critical step in GRN inference is distinguishing causality from correlation by identifying which genes are regulators and which are targets. This transforms an undirected co-expression graph into a directed regulatory network.

• Cis-regulatory motif enrichment scans target gene promoters for known transcription factor binding motifs to validate predicted edges
• Time-series or pseudotime data can reveal temporal precedence, where a transcription factor's expression change precedes its target's response
• RNA velocity information can be incorporated to orient edges based on the direction of transcriptional dynamics
• Methods like SCENIC use the i-cisTarget database to perform motif enrichment and prune false-positive edges lacking regulatory evidence

GRN inference algorithms identify regulons—sets of co-expressed target genes controlled by a common transcription factor. These regulons represent functional modules that execute specific cellular programs.

• A regulon activity score can be computed for each cell by aggregating the expression of all target genes, enabling cell state characterization
• Regulon analysis reveals master regulators that orchestrate cell identity transitions, such as differentiation or reprogramming
• SCENIC outputs an AUCell matrix quantifying regulon activity per cell, which can be used for downstream clustering and trajectory analysis
• Comparative regulon analysis across conditions identifies which regulatory programs are activated or silenced in disease states

Modern approaches leverage deep generative models to infer regulatory relationships from single-cell data while accounting for technical noise and batch effects.

• Variational autoencoders (VAEs) learn a latent representation of gene expression that can be decoded into regulatory interactions
• Graph neural networks (GNNs) explicitly model the network structure, treating genes as nodes and regulatory edges as learnable parameters
• Attention mechanisms borrowed from transformer architectures can identify which transcription factors most influence each target gene's expression
• Methods like DeepSEM and CNNC use neural networks to predict regulatory edges directly from expression data, capturing complex non-linear regulatory logic

GRN inference accuracy improves substantially when single-cell expression data is integrated with complementary epigenomic measurements that provide direct evidence of regulatory interactions.

• scATAC-seq data reveals open chromatin regions and transcription factor footprinting, validating predicted binding events
• Single-cell multiome assays simultaneously measure RNA expression and chromatin accessibility in the same cell, enabling paired regulatory inference
• ChIP-seq peaks from bulk experiments can be used as a gold standard to benchmark and calibrate inferred GRN edges
• CITE-seq surface protein data adds an additional layer of validation for cell-type-specific regulatory programs

Rigorous evaluation of inferred GRNs is essential, yet challenging due to the absence of complete ground-truth regulatory maps for most organisms and cell types.

• BEELINE is a standardized benchmarking framework that evaluates GRN inference methods on synthetic and curated experimental datasets using metrics like AUPRC and EPR
• Perturbation data from CRISPR knockouts or knockdowns provides causal validation by testing whether a transcription factor's removal alters predicted target gene expression
• Network motif analysis checks whether inferred networks contain known regulatory patterns such as feed-forward loops and autoregulatory motifs
• Cross-validation strategies hold out known regulatory edges during training and measure recovery rates to assess generalization performance

A computational ordering of single cells along a continuous developmental path based on transcriptomic similarity. Essential for understanding how regulatory logic changes during differentiation.

• Reconstructs dynamic processes from static snapshot data
• Algorithms include Monocle, Slingshot, and PAGA
• Enables identification of branch points where gene regulation diverges

A method that predicts the future transcriptional state of individual cells by modeling the ratio of unspliced precursor mRNA to mature spliced mRNA. This provides a directional vector on gene expression dynamics.

• Distinguishes transcriptional induction from repression
• Complements pseudotime by adding directionality
• Implemented in tools like scVelo and velocyto

The computational fusion of multiple single-cell data modalities into a unified latent representation. Combining scRNA-seq with scATAC-seq provides direct evidence of regulatory element accessibility alongside gene expression.

• Methods include MOFA+, Seurat WNN, and totalVI
• Links chromatin accessibility to transcription factor binding
• Enables cross-modality prediction of regulatory interactions

The computational prediction of intercellular signaling networks by analyzing the co-expression of ligands and their cognate receptors across different cell types. Regulatory networks are often driven by extracellular signals.

• Tools: CellChat, NicheNet, CellPhoneDB
• Integrates ligand-receptor databases with expression data
• Reveals how niche factors activate transcription factor cascades

Single-cell Assay for Transposase-Accessible Chromatin with sequencing. Profiles open chromatin regions genome-wide in individual cells, directly measuring the regulatory landscape.

• Identifies cis-regulatory elements like promoters and enhancers
• Enables motif footprinting to infer transcription factor occupancy
• Often paired with scRNA-seq for linked gene regulation analysis