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Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets
High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in thei...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122954/ https://www.ncbi.nlm.nih.gov/pubmed/35607614 http://dx.doi.org/10.1016/j.patter.2022.100473 |
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author | Bing, Xin Lovelace, Tyler Bunea, Florentina Wegkamp, Marten Kasturi, Sudhir Pai Singh, Harinder Benos, Panayiotis V. Das, Jishnu |
author_facet | Bing, Xin Lovelace, Tyler Bunea, Florentina Wegkamp, Marten Kasturi, Sudhir Pai Singh, Harinder Benos, Panayiotis V. Das, Jishnu |
author_sort | Bing, Xin |
collection | PubMed |
description | High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation. |
format | Online Article Text |
id | pubmed-9122954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-91229542022-05-22 Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets Bing, Xin Lovelace, Tyler Bunea, Florentina Wegkamp, Marten Kasturi, Sudhir Pai Singh, Harinder Benos, Panayiotis V. Das, Jishnu Patterns (N Y) Article High-dimensional cellular and molecular profiling of biological samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions, and their integration to infer causal relationships. Here, we present Essential Regression (ER), a novel latent-factor-regression-based interpretable machine-learning approach that addresses these problems by identifying latent factors and their likely cause-effect relationships with system-wide outcomes/properties of interest. ER can integrate many multi-omic datasets without structural or distributional assumptions regarding the data. It outperforms a range of state-of-the-art methods in terms of prediction. ER can be coupled with probabilistic graphical modeling, thereby strengthening the causal inferences. The utility of ER is demonstrated using multi-omic system immunology datasets to generate and validate novel cellular and molecular inferences in a wide range of contexts including immunosenescence and immune dysregulation. Elsevier 2022-03-24 /pmc/articles/PMC9122954/ /pubmed/35607614 http://dx.doi.org/10.1016/j.patter.2022.100473 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Bing, Xin Lovelace, Tyler Bunea, Florentina Wegkamp, Marten Kasturi, Sudhir Pai Singh, Harinder Benos, Panayiotis V. Das, Jishnu Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title | Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title_full | Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title_fullStr | Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title_full_unstemmed | Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title_short | Essential Regression: A generalizable framework for inferring causal latent factors from multi-omic datasets |
title_sort | essential regression: a generalizable framework for inferring causal latent factors from multi-omic datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122954/ https://www.ncbi.nlm.nih.gov/pubmed/35607614 http://dx.doi.org/10.1016/j.patter.2022.100473 |
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