CSCN: inference of cell-specific causal networks using single-cell RNA-seq data

Understanding gene regulation is fundamental to deciphering the coordinated activity of genes within cells. Although single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at cellular resolution, most gene network inference methods operate at the tissue or population level, thereby overlooking regulatory heterogeneity across individual cells. Recent approaches, such as Cell-Specific Network (CSN) and its extension c-CSN, attempt to construct gene networks at single-cell resolution, providing a more detailed view of the regulatory logic underlying individual cellular states. However, these methods remain limited by high false positive rates due to indirect associations and lack of directionality or causal interpretability.

To address these issues, we propose the Cell-Specific Causal Network (CSCN) framework, which infers directed, cell-specific gene regulatory relationships by explicitly modeling causality. CSCN combines causal discovery techniques with efficient computation using kd-trees and bitmap indexing to perform conditional independence testing, yielding sparse and interpretable causal graphs for each cell that effectively suppress indirect and spurious associations. Across nine scRNA-seq datasets, the Causal Katz Matrix (CKM) derived from CSCN provided more accurate and stable cell-state discrimination than expression-based and network-based baselines. CSCN-derived representations also preserved developmental structure, achieving the best trajectory performance in simulations and the strongest agreement with human embryo progression. Beyond RNA-only analysis, CSCN further generalized to paired PBMC multiome, CITE-seq, and spatial transcriptomic settings. Also, in controlled confounding simulations, CSCN consistently achieved the lowest false-positive rates relative to CSN and c-CSN.

The code is available at https://github.com/open17/CSCN.