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Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results

BACKGROUND: Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). H...

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Autores principales: Zhao, Shitao, Sun, Jianqiang, Shimizu, Kentaro, Kadota, Koji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831220/
https://www.ncbi.nlm.nih.gov/pubmed/29507534
http://dx.doi.org/10.1186/s12575-018-0067-8
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author Zhao, Shitao
Sun, Jianqiang
Shimizu, Kentaro
Kadota, Koji
author_facet Zhao, Shitao
Sun, Jianqiang
Shimizu, Kentaro
Kadota, Koji
author_sort Zhao, Shitao
collection PubMed
description BACKGROUND: Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). However, the evaluation of arbitrary defined groups still counts in subjective visual inspection. RESULTS: To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to − 1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger (or smaller) AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (P(DEG)). We also found that the AS values were generally independent on the number of replicates (N(rep)). Although the P(DEG) values depended on N(rep), we confirmed that both AS and P(DEG) values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram. CONCLUSION: Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12575-018-0067-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-58312202018-03-05 Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results Zhao, Shitao Sun, Jianqiang Shimizu, Kentaro Kadota, Koji Biol Proced Online Research BACKGROUND: Hierarchical Sample clustering (HSC) is widely performed to examine associations within expression data obtained from microarrays and RNA sequencing (RNA-seq). Researchers have investigated the HSC results with several possible criteria for grouping (e.g., sex, age, and disease types). However, the evaluation of arbitrary defined groups still counts in subjective visual inspection. RESULTS: To objectively evaluate the degree of separation between groups of interest in the HSC dendrogram, we propose to use Silhouette scores. Silhouettes was originally developed as a graphical aid for the validation of data clusters. It provides a measure of how well a sample is classified when it was assigned to a cluster by according to both the tightness of the clusters and the separation between them. It ranges from 1.0 to − 1.0, and a larger value for the average silhouette (AS) over all samples to be analyzed indicates a higher degree of cluster separation. The basic idea to use an AS is to replace the term cluster by group when calculating the scores. We investigated the validity of this score using simulated and real data designed for differential expression (DE) analysis. We found that larger (or smaller) AS values agreed well with both higher (or lower) degrees of separation between different groups and higher percentages of differentially expressed genes (P(DEG)). We also found that the AS values were generally independent on the number of replicates (N(rep)). Although the P(DEG) values depended on N(rep), we confirmed that both AS and P(DEG) values were close to zero when samples in the data showed an intermingled nature between the groups in the HSC dendrogram. CONCLUSION: Silhouettes is useful for exploring data with predefined group labels. It would help provide both an objective evaluation of HSC dendrograms and insights into the DE results with regard to the compared groups. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12575-018-0067-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-03-01 /pmc/articles/PMC5831220/ /pubmed/29507534 http://dx.doi.org/10.1186/s12575-018-0067-8 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Zhao, Shitao
Sun, Jianqiang
Shimizu, Kentaro
Kadota, Koji
Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title_full Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title_fullStr Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title_full_unstemmed Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title_short Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results
title_sort silhouette scores for arbitrary defined groups in gene expression data and insights into differential expression results
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5831220/
https://www.ncbi.nlm.nih.gov/pubmed/29507534
http://dx.doi.org/10.1186/s12575-018-0067-8
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