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Finding associations in a heterogeneous setting: statistical test for aberration enrichment

Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such ef...

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Detalles Bibliográficos
Autores principales: Mezlini, Aziz M., Das, Sudeshna, Goldenberg, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066476/
https://www.ncbi.nlm.nih.gov/pubmed/33892787
http://dx.doi.org/10.1186/s13073-021-00864-4
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author Mezlini, Aziz M.
Das, Sudeshna
Goldenberg, Anna
author_facet Mezlini, Aziz M.
Das, Sudeshna
Goldenberg, Anna
author_sort Mezlini, Aziz M.
collection PubMed
description Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13073-021-00864-4).
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spelling pubmed-80664762021-04-26 Finding associations in a heterogeneous setting: statistical test for aberration enrichment Mezlini, Aziz M. Das, Sudeshna Goldenberg, Anna Genome Med Method Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13073-021-00864-4). BioMed Central 2021-04-23 /pmc/articles/PMC8066476/ /pubmed/33892787 http://dx.doi.org/10.1186/s13073-021-00864-4 Text en © The Author(s) 2021 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 Method
Mezlini, Aziz M.
Das, Sudeshna
Goldenberg, Anna
Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title_full Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title_fullStr Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title_full_unstemmed Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title_short Finding associations in a heterogeneous setting: statistical test for aberration enrichment
title_sort finding associations in a heterogeneous setting: statistical test for aberration enrichment
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066476/
https://www.ncbi.nlm.nih.gov/pubmed/33892787
http://dx.doi.org/10.1186/s13073-021-00864-4
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