Sara Mohammad-Taheri1, Jeremy Zucker2, Charles Tapley Hoyt3, Karen Sachs4,5, Vartika Tewari1, Robert Ness6, Olga Vitek1. 1. Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA. 2. Computational Biology, Pacific Northwest National Laboratory, Richland, Washington, DC 99354, USA. 3. Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA. 4. Next Generation Analytics, Palo Alto, CA 94301, USA. 5. Answer ALS Consortium, LA, CA 70184, USA. 6. Microsoft Research, Redmond, WA 98052, USA.
Abstract
MOTIVATION: Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. RESULTS: In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. AVAILABILITY AND IMPLEMENTATION: The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. RESULTS: In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. AVAILABILITY AND IMPLEMENTATION: The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Andrew D McNaughton; Erin L Bredeweg; James Manzer; Jeremy Zucker; Nathalie Munoz Munoz; Meagan C Burnet; Ernesto S Nakayasu; Kyle R Pomraning; Eric D Merkley; Ziyu Dai; William B Chrisler; Scott E Baker; Peter C St John; Neeraj Kumar Journal: ACS Synth Biol Date: 2021-10-12 Impact factor: 5.110