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Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information
BACKGROUND: Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin ac...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664258/ https://www.ncbi.nlm.nih.gov/pubmed/37990143 http://dx.doi.org/10.1186/s12859-023-05553-0 |
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author | Roth, Cullen Venu, Vrinda Job, Vanessa Lubbers, Nicholas Sanbonmatsu, Karissa Y. Steadman, Christina R. Starkenburg, Shawn R. |
author_facet | Roth, Cullen Venu, Vrinda Job, Vanessa Lubbers, Nicholas Sanbonmatsu, Karissa Y. Steadman, Christina R. Starkenburg, Shawn R. |
author_sort | Roth, Cullen |
collection | PubMed |
description | BACKGROUND: Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. RESULTS: Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s [Formula: see text] coefficients as well as Kendall’s [Formula: see text] and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R[Formula: see text] , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spearman’s [Formula: see text] , Kendall’s [Formula: see text] , and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R[Formula: see text] coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. CONCLUSIONS: Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays. |
format | Online Article Text |
id | pubmed-10664258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106642582023-11-22 Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information Roth, Cullen Venu, Vrinda Job, Vanessa Lubbers, Nicholas Sanbonmatsu, Karissa Y. Steadman, Christina R. Starkenburg, Shawn R. BMC Bioinformatics Research BACKGROUND: Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. RESULTS: Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s [Formula: see text] coefficients as well as Kendall’s [Formula: see text] and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R[Formula: see text] , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spearman’s [Formula: see text] , Kendall’s [Formula: see text] , and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R[Formula: see text] coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. CONCLUSIONS: Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays. BioMed Central 2023-11-22 /pmc/articles/PMC10664258/ /pubmed/37990143 http://dx.doi.org/10.1186/s12859-023-05553-0 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Roth, Cullen Venu, Vrinda Job, Vanessa Lubbers, Nicholas Sanbonmatsu, Karissa Y. Steadman, Christina R. Starkenburg, Shawn R. Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title | Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title_full | Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title_fullStr | Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title_full_unstemmed | Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title_short | Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
title_sort | improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664258/ https://www.ncbi.nlm.nih.gov/pubmed/37990143 http://dx.doi.org/10.1186/s12859-023-05553-0 |
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