Cargando…
sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic
Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expre...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169426/ https://www.ncbi.nlm.nih.gov/pubmed/32346383 http://dx.doi.org/10.3389/fgene.2020.00247 |
_version_ | 1783523787071815680 |
---|---|
author | Das, Pijush Roychowdhury, Anirban Das, Subhadeep Roychoudhury, Susanta Tripathy, Sucheta |
author_facet | Das, Pijush Roychowdhury, Anirban Das, Subhadeep Roychoudhury, Susanta Tripathy, Sucheta |
author_sort | Das, Pijush |
collection | PubMed |
description | Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as “sigFeature” (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of “sigFeature” to three other feature selection algorithms. A small number of selected features (by “sigFeature”) also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from “sigFeature” and compared it with the outputs of other algorithms. We observed that “sigFeature” is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, “sigFeature” is considerably better than related algorithms in discovering differentially significant features from microarray data sets. |
format | Online Article Text |
id | pubmed-7169426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71694262020-04-28 sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic Das, Pijush Roychowdhury, Anirban Das, Subhadeep Roychoudhury, Susanta Tripathy, Sucheta Front Genet Genetics Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as “sigFeature” (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The “sigFeature” R package is centered around a function called “sigFeature,” which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of “sigFeature” to three other feature selection algorithms. A small number of selected features (by “sigFeature”) also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from “sigFeature” and compared it with the outputs of other algorithms. We observed that “sigFeature” is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, “sigFeature” is considerably better than related algorithms in discovering differentially significant features from microarray data sets. Frontiers Media S.A. 2020-04-03 /pmc/articles/PMC7169426/ /pubmed/32346383 http://dx.doi.org/10.3389/fgene.2020.00247 Text en Copyright © 2020 Das, Roychowdhury, Das, Roychoudhury and Tripathy. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Das, Pijush Roychowdhury, Anirban Das, Subhadeep Roychoudhury, Susanta Tripathy, Sucheta sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title | sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title_full | sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title_fullStr | sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title_full_unstemmed | sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title_short | sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic |
title_sort | sigfeature: novel significant feature selection method for classification of gene expression data using support vector machine and t statistic |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169426/ https://www.ncbi.nlm.nih.gov/pubmed/32346383 http://dx.doi.org/10.3389/fgene.2020.00247 |
work_keys_str_mv | AT daspijush sigfeaturenovelsignificantfeatureselectionmethodforclassificationofgeneexpressiondatausingsupportvectormachineandtstatistic AT roychowdhuryanirban sigfeaturenovelsignificantfeatureselectionmethodforclassificationofgeneexpressiondatausingsupportvectormachineandtstatistic AT dassubhadeep sigfeaturenovelsignificantfeatureselectionmethodforclassificationofgeneexpressiondatausingsupportvectormachineandtstatistic AT roychoudhurysusanta sigfeaturenovelsignificantfeatureselectionmethodforclassificationofgeneexpressiondatausingsupportvectormachineandtstatistic AT tripathysucheta sigfeaturenovelsignificantfeatureselectionmethodforclassificationofgeneexpressiondatausingsupportvectormachineandtstatistic |