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Linking co-expression modules with phenotypes
The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost d...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997497/ https://www.ncbi.nlm.nih.gov/pubmed/36909689 http://dx.doi.org/10.6026/97320630018438 |
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author | Kumar, Rakesh Ojha, Krishna Kumar Yadav, Harlokesh Narayan Singh, Vijay Kumar |
author_facet | Kumar, Rakesh Ojha, Krishna Kumar Yadav, Harlokesh Narayan Singh, Vijay Kumar |
author_sort | Kumar, Rakesh |
collection | PubMed |
description | The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status. |
format | Online Article Text |
id | pubmed-9997497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-99974972023-03-10 Linking co-expression modules with phenotypes Kumar, Rakesh Ojha, Krishna Kumar Yadav, Harlokesh Narayan Singh, Vijay Kumar Bioinformation Research Article The method for quantifying the association between co-expression module and clinical trait of interest requires application of dimensionality reduction to summaries modules as one dimensional (1D) vector. However, these methods are often linked with information loss. The amount of information lost depends upon the percentage of variance captured by the reduced 1D vector. Therefore, it is of interest to describe a method using analysis of rank (AOR) to assess the association between module and clinical trait of interest. This method works with clinical traits represented as binary class labels and can be adopted for clinical traits measured in continuous scale by dividing samples in two groups around median value. Application of the AOR method on test data for muscle gene expression profiles identifies modules significantly associated with diabetes status. Biomedical Informatics 2022-04-30 /pmc/articles/PMC9997497/ /pubmed/36909689 http://dx.doi.org/10.6026/97320630018438 Text en © 2022 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License. |
spellingShingle | Research Article Kumar, Rakesh Ojha, Krishna Kumar Yadav, Harlokesh Narayan Singh, Vijay Kumar Linking co-expression modules with phenotypes |
title | Linking co-expression modules with phenotypes |
title_full | Linking co-expression modules with phenotypes |
title_fullStr | Linking co-expression modules with phenotypes |
title_full_unstemmed | Linking co-expression modules with phenotypes |
title_short | Linking co-expression modules with phenotypes |
title_sort | linking co-expression modules with phenotypes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9997497/ https://www.ncbi.nlm.nih.gov/pubmed/36909689 http://dx.doi.org/10.6026/97320630018438 |
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