Year: 2021
Type: article
Abstract: In this dissertation, we present the result of the study on improving the explanation of cluster analysis for transcriptome data.To improve the explanation of cluster analysis, it is necessary to perform accurate cluster analysis and select features that contribute to cluster separation.In cluster analysis that integrates multiple instances of transcriptome data, the batch effect deteriorates the analysis performance.To perform highly accurate cluster analysis, we propose a batch effect correction method based on the dimensionality reduction method.In addition, the transcriptome data have numerous features; that is, it is difficult to explain and interpret the cluster analysis results.Accordingly, we also propose a feature selection method that is useful for cluster analysis.
Authors: 萌望 城取, モモ シロトリ, Momo Shirotori
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W3195692064