Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples

Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a maj...

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Autores principales:	Thanh M. Nguyen, Samuel Bharti, Zongliang Yue, Christopher D. Willey, Jake Y. Chen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	sample enrichment analysis clinotype SEAS glioblastoma multiforme patient-derived xenograft Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/f51a8838d56f47ff861e6f335e441693
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id	oai:doaj.org-article:f51a8838d56f47ff861e6f335e441693
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spelling	oai:doaj.org-article:f51a8838d56f47ff861e6f335e4416932021-11-12T12:02:45ZStatistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples2624-909X10.3389/fdata.2021.725276https://doaj.org/article/f51a8838d56f47ff861e6f335e4416932021-09-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fdata.2021.725276/fullhttps://doaj.org/toc/2624-909XUnsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as “clinotypes,” such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)—patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.Thanh M. NguyenSamuel BhartiZongliang YueChristopher D. WilleyJake Y. ChenFrontiers Media S.A.articlesample enrichment analysisclinotypeSEASglioblastoma multiformepatient-derived xenograftpatient-derived xenograftInformation technologyT58.5-58.64ENFrontiers in Big Data, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sample enrichment analysis clinotype SEAS glioblastoma multiforme patient-derived xenograft patient-derived xenograft Information technology T58.5-58.64
spellingShingle	sample enrichment analysis clinotype SEAS glioblastoma multiforme patient-derived xenograft patient-derived xenograft Information technology T58.5-58.64 Thanh M. Nguyen Samuel Bharti Zongliang Yue Christopher D. Willey Jake Y. Chen Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
description	Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as “clinotypes,” such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)—patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.
format	article
author	Thanh M. Nguyen Samuel Bharti Zongliang Yue Christopher D. Willey Jake Y. Chen
author_facet	Thanh M. Nguyen Samuel Bharti Zongliang Yue Christopher D. Willey Jake Y. Chen
author_sort	Thanh M. Nguyen
title	Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
title_short	Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
title_full	Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
title_fullStr	Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
title_full_unstemmed	Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples
title_sort	statistical enrichment analysis of samples: a general-purpose tool to annotate metadata neighborhoods of biological samples
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f51a8838d56f47ff861e6f335e441693
work_keys_str_mv	AT thanhmnguyen statisticalenrichmentanalysisofsamplesageneralpurposetooltoannotatemetadataneighborhoodsofbiologicalsamples AT samuelbharti statisticalenrichmentanalysisofsamplesageneralpurposetooltoannotatemetadataneighborhoodsofbiologicalsamples AT zongliangyue statisticalenrichmentanalysisofsamplesageneralpurposetooltoannotatemetadataneighborhoodsofbiologicalsamples AT christopherdwilley statisticalenrichmentanalysisofsamplesageneralpurposetooltoannotatemetadataneighborhoodsofbiologicalsamples AT jakeychen statisticalenrichmentanalysisofsamplesageneralpurposetooltoannotatemetadataneighborhoodsofbiologicalsamples
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Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples

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