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Correlated gene modules uncovered by high-precision single-cell transcriptomics
Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9907105/ https://www.ncbi.nlm.nih.gov/pubmed/36508663 http://dx.doi.org/10.1073/pnas.2206938119 |
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author | Chapman, Alec R. Lee, David F. Cai, Wenting Ma, Wenping Li, Xiang Sun, Wenjie Xie, Xiaoliang Sunney |
author_facet | Chapman, Alec R. Lee, David F. Cai, Wenting Ma, Wenping Li, Xiang Sun, Wenjie Xie, Xiaoliang Sunney |
author_sort | Chapman, Alec R. |
collection | PubMed |
description | Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome. |
format | Online Article Text |
id | pubmed-9907105 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-99071052023-02-08 Correlated gene modules uncovered by high-precision single-cell transcriptomics Chapman, Alec R. Lee, David F. Cai, Wenting Ma, Wenping Li, Xiang Sun, Wenjie Xie, Xiaoliang Sunney Proc Natl Acad Sci U S A Biological Sciences Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome. National Academy of Sciences 2022-12-12 2022-12-20 /pmc/articles/PMC9907105/ /pubmed/36508663 http://dx.doi.org/10.1073/pnas.2206938119 Text en Copyright © 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Biological Sciences Chapman, Alec R. Lee, David F. Cai, Wenting Ma, Wenping Li, Xiang Sun, Wenjie Xie, Xiaoliang Sunney Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title | Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title_full | Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title_fullStr | Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title_full_unstemmed | Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title_short | Correlated gene modules uncovered by high-precision single-cell transcriptomics |
title_sort | correlated gene modules uncovered by high-precision single-cell transcriptomics |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9907105/ https://www.ncbi.nlm.nih.gov/pubmed/36508663 http://dx.doi.org/10.1073/pnas.2206938119 |
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