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However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce causarray, a robust causal inference framework for analyzing array-based genomic data at both pseudo-bulk and single-cell levels under unmeasured confounding. causarray integrates a generalized confounder adjustment method to account for unmeasured confounders and employs semiparametric inference with flexible machine learning techniques to ensure robust statistical estimation of treatment effects. Benchmarking results show that causarray robustly separates treatment effects from confounders while preserving biological signals across diverse settings. We also apply causarray to two single-cell genomic studies: (i) an in vivo Perturb-seq study of autism risk genes in developing mouse brains and (ii) a case-control study of Alzheimer\u2019s disease (AD) using three human brain transcriptomic datasets. In these applications, causarray identifies clustered causal effects of multiple autism risk genes and consistent causally affected genes across AD datasets, uncovering biologically relevant pathways directly linked to neuronal development and synaptic functions that are critical for understanding disease pathology.<\/jats:p>","DOI":"10.1093\/bib\/bbag175","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:22:51Z","timestamp":1774268571000},"source":"Crossref","is-referenced-by-count":4,"title":["Uncovering causal relationships in single-cell omic studies with causarray"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9683-4146","authenticated-orcid":false,"given":"Jin-Hong","family":"Du","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial Science, The University of Hong Kong , Pok Fu Lam, Hong Kong SAR 00000 ,","place":["China"]},{"name":"Musketeers Foundation Institute of Data Science, The University of Hong Kong , Pok Fu Lam, Hong Kong SAR 00000 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maya","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , 5000 Forbes Ave, Pittsburgh, PA 15213 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hansruedi","family":"Mathys","sequence":"additional","affiliation":[{"name":"Department of Neurobiology, University of Pittsburgh , 4200 Fifth Ave, Pittsburgh, PA 15261 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn","family":"Roeder","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Carnegie Mellon University , 5000 Forbes Ave, Pittsburgh, PA 15213 ,","place":["United States"]},{"name":"Computational Biology Department, Carnegie Mellon University , 5000 Forbes Ave, Pittsburgh, PA 15213 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,15]]},"reference":[{"key":"2026041513282995600_ref1","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1038\/nprot.2017.149","article-title":"Exponential scaling of single-cell RNA-seq in the past decade","volume":"13","author":"Svensson","year":"2018","journal-title":"Nat Protoc"},{"key":"2026041513282995600_ref2","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1126\/science.aad0501","article-title":"Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq","volume":"352","author":"Tirosh","year":"2016","journal-title":"Science"},{"key":"2026041513282995600_ref3","doi-asserted-by":"publisher","first-page":"e8746","DOI":"10.15252\/msb.20188746","article-title":"Current best practices in single-cell RNA-seq analysis: a tutorial","volume":"15","author":"Luecken","year":"2019","journal-title":"Mol Syst Biol"},{"key":"2026041513282995600_ref4","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1038\/s41576-023-00628-3","article-title":"Editorial. 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