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A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis
BACKGROUND: Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Dat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6615984/ https://www.ncbi.nlm.nih.gov/pubmed/31289834 http://dx.doi.org/10.1093/gigascience/giz080 |
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author | Lee, Eunjee Yoo, Seungyeul Wang, Wenhui Tu, Zhidong Zhu, Jun |
author_facet | Lee, Eunjee Yoo, Seungyeul Wang, Wenhui Tu, Zhidong Zhu, Jun |
author_sort | Lee, Eunjee |
collection | PubMed |
description | BACKGROUND: Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases. RESULTS: Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis. CONCLUSIONS: Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis. |
format | Online Article Text |
id | pubmed-6615984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-66159842019-07-15 A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis Lee, Eunjee Yoo, Seungyeul Wang, Wenhui Tu, Zhidong Zhu, Jun Gigascience Research BACKGROUND: Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases. RESULTS: Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis-associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis. CONCLUSIONS: Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis. Oxford University Press 2019-07-09 /pmc/articles/PMC6615984/ /pubmed/31289834 http://dx.doi.org/10.1093/gigascience/giz080 Text en © The Author(s) 2019. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Lee, Eunjee Yoo, Seungyeul Wang, Wenhui Tu, Zhidong Zhu, Jun A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title | A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title_full | A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title_fullStr | A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title_full_unstemmed | A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title_short | A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
title_sort | probabilistic multi-omics data matching method for detecting sample errors in integrative analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6615984/ https://www.ncbi.nlm.nih.gov/pubmed/31289834 http://dx.doi.org/10.1093/gigascience/giz080 |
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