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Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE). Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0003-2386-930x
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2021 (English)In: Frontiers in Genetics, E-ISSN 1664-8021, Vol. 12, article id 632620Article in journal (Refereed) Published
Abstract [en]

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

Place, publisher, year, edition, pages
Frontiers Media S.A. , 2021. Vol. 12, article id 632620
Keywords [en]
cancer subtypes, feature selection, gene selection, high-dimensional, RNA-seq
National Category
Probability Theory and Statistics Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:umu:diva-181727DOI: 10.3389/fgene.2021.632620ISI: 000626903100001Scopus ID: 2-s2.0-85102373666OAI: oai:DiVA.org:umu-181727DiVA, id: diva2:1539593
Available from: 2021-03-24 Created: 2021-03-24 Last updated: 2023-09-05Bibliographically approved

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Källberg, DavidVidman, LindaRydén, Patrik

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Umeå School of Business and Economics (USBE)Department of Mathematics and Mathematical StatisticsOncology
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