# Gene Regulatory Network Inference

Inferring gene regulatory networks from single-cell RNA sequencing has long split into two routes: graph methods train on the whole network as one graph and tend to overfit, while pairwise methods split it into transcription factor–target pairs, gaining abundant samples but losing global topology. scGPL uses a dual-branch design to model both the global regulatory graph and a local subgraph around each candidate pair, reaching the best performance on seven benchmark datasets, with AUPRC up to 13.7% above the strongest baseline.

Inferring gene regulatory networks from single-cell RNA sequencing has long split into two routes: graph methods train on the whole network as one graph and tend to overfit, while pairwise methods split it into transcription factor–target pairs, gaining abundant samples but losing global topology. scGPL uses a dual-branch design to model both the global regulatory graph and a local subgraph around each candidate pair, reaching the best performance on seven benchmark datasets, with AUPRC up to 13.7% above the strongest baseline.

Across 3.47 million peripheral blood mononuclear cell nuclei from 922 donors, chromatin accessibility was treated as a heritable molecular trait, yielding 243,225 chromatin accessibility quantitative trait loci (caQTLs) across 28 immune cell types, 60% of them cell-type specific. Compared with single-cell expression QTLs (eQTLs), these loci colocalized with 4.5–22.6% more genome-wide association signals for 16 diseases and 44 blood traits; adding cell-type context, wider windows and multi-causal-variant models raised the share of inflammatory bowel disease loci with colocalization evidence from about 16% to about 65%.

Across 3.47 million peripheral blood mononuclear cell nuclei from 922 donors, chromatin accessibility was treated as a heritable molecular trait, yielding 243,225 chromatin accessibility quantitative trait loci (caQTLs) across 28 immune cell types, 60% of them cell-type specific. Compared with single-cell expression QTLs (eQTLs), these loci colocalized with 4.5–22.6% more genome-wide association signals for 16 diseases and 44 blood traits; adding cell-type context, wider windows and multi-causal-variant models raised the share of inflammatory bowel disease loci with colocalization evidence from about 16% to about 65%.

Regulatory network inference no longer needs cell types called first. Earlier pipelines clustered cells, annotated types with marker genes, then inferred one network per cluster, so clustering and annotation errors propagated into the network. NetMap infers a gene regulatory network per cell before any clustering, uses GradientShap to attribute network edges to input genes, and reaches clustering scores of 1.0, 0.99 and 0.99 across three PBMC multiome evaluation cases.

Regulatory network inference no longer needs cell types called first. Earlier pipelines clustered cells, annotated types with marker genes, then inferred one network per cluster, so clustering and annotation errors propagated into the network. NetMap infers a gene regulatory network per cell before any clustering, uses GradientShap to attribute network edges to input genes, and reaches clustering scores of 1.0, 0.99 and 0.99 across three PBMC multiome evaluation cases.