Detecting differentially expressed genes from RNA-seq data using fuzzy clustering

Ando Yuki () and Shimokawa Asanao
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Ando Yuki: 26413 Tokyo University of Science , Shinjuku-ku, 162-8601, Tokyo, Japan
Shimokawa Asanao: Department of Mathematics, 26413 Tokyo University of Science , 1-3 Kagurazaka, Shinjuku-ku, 162-8601, Tokyo, Japan

The International Journal of Biostatistics, 2024, vol. 20, issue 2, 407-417

Abstract: A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased.

Keywords: two group comparison; DEGs; expression level; fold-change (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2023-0125

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