dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data
Summary: New assays such as Perturb-seq link parallel CRISPR interventions to transcriptomic readouts, providing insight into gene regulatory networks. Causal regulatory networks can be represented by directed acyclic graphs (DAGs), but lack of identifiability and a combinatorial solution space comp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224029006 |
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| author | Albert Xue Jingyou Rao Sriram Sankararaman Harold Pimentel |
| author_facet | Albert Xue Jingyou Rao Sriram Sankararaman Harold Pimentel |
| author_sort | Albert Xue |
| collection | DOAJ |
| description | Summary: New assays such as Perturb-seq link parallel CRISPR interventions to transcriptomic readouts, providing insight into gene regulatory networks. Causal regulatory networks can be represented by directed acyclic graphs (DAGs), but lack of identifiability and a combinatorial solution space complicate learning DAGs from observational data. Score-based methods have improved the practical scalability of inferring DAGs, but are sensitive to error variance structure. Furthermore, correction for error variance is difficult without prior knowledge of structure. We present dotears [doo-tairs], a continuous optimization framework leveraging observational and interventional data to infer causal structure, assuming a linear Structural Equation Model. dotears exploits structural consequences of hard interventions to estimate and correct for error variance structure. dotears is a provably consistent estimator of the true DAG under mild assumptions and outperforms other state-of-the-art methods in varied simulations. In real data, differential expression tests and high-confidence protein-protein interactions validate dotears-inferred edges with higher precision and recall than others. |
| format | Article |
| id | doaj-art-a7006f28697e4f4cb300e0179eb1850a |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-a7006f28697e4f4cb300e0179eb1850a2025-01-30T05:14:48ZengElsevieriScience2589-00422025-02-01282111673dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional dataAlbert Xue0Jingyou Rao1Sriram Sankararaman2Harold Pimentel3Bioinformatics Indepartmental Program, UCLA, Los Angeles, CA 90024, USA; Corresponding authorDepartment of Computer Science, UCLA, Los Angeles, CA 90024, USADepartment of Computer Science, UCLA, Los Angeles, CA 90024, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA; Corresponding authorDepartment of Computer Science, UCLA, Los Angeles, CA 90024, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90024, USA; Corresponding authorSummary: New assays such as Perturb-seq link parallel CRISPR interventions to transcriptomic readouts, providing insight into gene regulatory networks. Causal regulatory networks can be represented by directed acyclic graphs (DAGs), but lack of identifiability and a combinatorial solution space complicate learning DAGs from observational data. Score-based methods have improved the practical scalability of inferring DAGs, but are sensitive to error variance structure. Furthermore, correction for error variance is difficult without prior knowledge of structure. We present dotears [doo-tairs], a continuous optimization framework leveraging observational and interventional data to infer causal structure, assuming a linear Structural Equation Model. dotears exploits structural consequences of hard interventions to estimate and correct for error variance structure. dotears is a provably consistent estimator of the true DAG under mild assumptions and outperforms other state-of-the-art methods in varied simulations. In real data, differential expression tests and high-confidence protein-protein interactions validate dotears-inferred edges with higher precision and recall than others.http://www.sciencedirect.com/science/article/pii/S2589004224029006Gene networkBioinformaticsBiocomputational method |
| spellingShingle | Albert Xue Jingyou Rao Sriram Sankararaman Harold Pimentel dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data iScience Gene network Bioinformatics Biocomputational method |
| title | dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data |
| title_full | dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data |
| title_fullStr | dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data |
| title_full_unstemmed | dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data |
| title_short | dotears: Scalable and consistent directed acyclic graph estimation using observational and interventional data |
| title_sort | dotears scalable and consistent directed acyclic graph estimation using observational and interventional data |
| topic | Gene network Bioinformatics Biocomputational method |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224029006 |
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