Beyond correlation: Causal discovery of cell program interactions in lung tissue

Abstract

Cell-cell communication networks govern tissue function in health and disease, yet existing computational tools rely on static ligand-receptor databases or correlation-based analyses that cannot distinguish direct causal relationships from confounded or indirect associations. We introduce Cell-CLIP (Cellular Causal Learning and Inference Pipeline), a modular framework that combines (i) mask-aware patient-level JASMINE activity scoring with low-percentile imputation in raw activity space, (ii) five complementary causal-discovery algorithms -- PC, GES, FCI, DirectLiNGAM, and GRaSP -- each fed an algorithm-matched per-column transform of the same masked JASMINE matrix, (iii) direction-preserving bootstrap aggregation with a weighted two-phase consensus, (iv) Joint Causal Inference (JCI) for principled pooling of patient cohorts via context indicators, and (v) a residual-independence (RESIT) direction audit, all evaluated under a systematic three-by-three-by-three hyperparameter sweep. Applied to a 611-patient multi-phenotype lung atlas spanning normal tissue, COVID-19 pneumonia, and lung cancer, the cross-cohort joint-causal-inference graph constructed by stratifying patients into normal, COVID-19, and tumour contexts before pooling recovers 100% of evaluable curated ground-truth edges (8/8; Wilson 95% CI 0.68-1.00) with 75.0% direction accuracy (6/8; 95% CI 0.41-0.95) and zero forbidden-orientation violations (0/8; 95% CI 0.00-0.32) -- the only configuration in a comprehensive ablation series spanning 7 cohort/pool configurations, 27 hyperparameter settings, and 3 consensus types to simultaneously reach all three optimal Pareto corners. The single-cohort universal cross-phenotype graph (12 cross-phenotype programs over the full 611-patient atlas) similarly achieves 100% evaluable recall (8/8; 95% CI 0.68-1.00) with 62.5% direction accuracy (5/8; 95% CI 0.31-0.86) and zero forbidden violations (0/3; 95% CI 0.00-0.56) under the same systematic hyperparameter sweep with low-percentile imputation, exceeding an earlier zero-imputation baseline configuration of the pipeline on every reported metric (Table 6, Table 8); given small ground-truth denominators (n_eval = 8), these point-estimate improvements lie within the Wilson 95% intervals of the baseline and should be read as direction-of-effect rather than as confirmatory comparisons. We further show that single-cohort causal inference on the disease-extended pooled cohorts (which apply the COVID- and cancer-extended program sets to all 611 patients) recovers strong skeleton recall (35.0%-55.2% evaluable) but suffers a structural direction collapse (down to 14.3% direction accuracy and 60% forbidden rate at union) caused by within-cohort averaging over heterogeneous normal-plus-disease patient populations; the patient-stratified joint-causal-inference pool dissolves this collapse without altering the underlying algorithms. Cell-CLIP provides a hypothesis-generating framework for inferring directed cell-program networks from patient-level scRNA-seq atlases, with all reported relationships requiring experimental validation due to observational data limitations. O_TBL View this table: org.highwire.dtl.DTLVardef@1b18810org.highwire.dtl.DTLVardef@5c027eorg.highwire.dtl.DTLVardef@a7b806org.highwire.dtl.DTLVardef@122adf2org.highwire.dtl.DTLVardef@1ca0a39_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 6C_FLOATNO O_TABLECAPTIONHeadline validation metrics across reporting cohorts, with Wilson 95% confidence intervals on every binomial rate. C_TABLECAPTION C_TBL O_TBL View this table: org.highwire.dtl.DTLVardef@1653cb1org.highwire.dtl.DTLVardef@173c6baorg.highwire.dtl.DTLVardef@1fc115forg.highwire.dtl.DTLVardef@1d87e39org.highwire.dtl.DTLVardef@46d53b_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 8C_FLOATNO O_TABLECAPTIONAblation matrix: evaluable recall at the recall-best configuration. Variants are defined in the Sensitivity analysis section. Cells marked not run were not executed for that combination of cohort x variant; reasons are given in the table caption. C_TABLECAPTION C_TBL