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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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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). |
format | Online Article Text |
id | pubmed-8066476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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