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dc.contributor.authorPark, Yongjin P.
dc.contributor.authorKellis, Manolis
dc.date.accessioned2022-05-18T15:41:51Z
dc.date.available2021-11-01T14:33:55Z
dc.date.available2022-05-18T15:41:51Z
dc.date.issued2021-08
dc.date.submitted2021-01
dc.identifier.issn1474-760X
dc.identifier.urihttps://hdl.handle.net/1721.1/136872.2
dc.description.abstractAbstract Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer’s disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttps://doi.org/10.1186/s13059-021-02438-4en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.titleCoCoA-diff: counterfactual inference for single-cell gene expression analysisen_US
dc.typeArticleen_US
dc.identifier.citationGenome Biology. 2021 Aug 17;22(1):228en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalGenome Biologyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-08-22T03:11:05Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2021-08-22T03:11:05Z
mit.journal.volume22en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work Neededen_US


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