Evaluation and comparison of multi-omics data integration methods for cancer subtyping.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, que...

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Autores principales:	Ran Duan, Lin Gao, Yong Gao, Yuxuan Hu, Han Xu, Mingfeng Huang, Kuo Song, Hongda Wang, Yongqiang Dong, Chaoqun Jiang, Chenxing Zhang, Songwei Jia
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6a6bcaa0ec3c4c73a360fc6dbd6f0d0f
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spelling	oai:doaj.org-article:6a6bcaa0ec3c4c73a360fc6dbd6f0d0f2021-12-02T19:58:06ZEvaluation and comparison of multi-omics data integration methods for cancer subtyping.1553-734X1553-735810.1371/journal.pcbi.1009224https://doaj.org/article/6a6bcaa0ec3c4c73a360fc6dbd6f0d0f2021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009224https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.Ran DuanLin GaoYong GaoYuxuan HuHan XuMingfeng HuangKuo SongHongda WangYongqiang DongChaoqun JiangChenxing ZhangSongwei JiaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009224 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ran Duan Lin Gao Yong Gao Yuxuan Hu Han Xu Mingfeng Huang Kuo Song Hongda Wang Yongqiang Dong Chaoqun Jiang Chenxing Zhang Songwei Jia Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
description	Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.
format	article
author	Ran Duan Lin Gao Yong Gao Yuxuan Hu Han Xu Mingfeng Huang Kuo Song Hongda Wang Yongqiang Dong Chaoqun Jiang Chenxing Zhang Songwei Jia
author_facet	Ran Duan Lin Gao Yong Gao Yuxuan Hu Han Xu Mingfeng Huang Kuo Song Hongda Wang Yongqiang Dong Chaoqun Jiang Chenxing Zhang Songwei Jia
author_sort	Ran Duan
title	Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
title_short	Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
title_full	Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
title_fullStr	Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
title_full_unstemmed	Evaluation and comparison of multi-omics data integration methods for cancer subtyping.
title_sort	evaluation and comparison of multi-omics data integration methods for cancer subtyping.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6a6bcaa0ec3c4c73a360fc6dbd6f0d0f
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Evaluation and comparison of multi-omics data integration methods for cancer subtyping.

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