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Updated: Jun 28, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Gene expression data classification using consensus independent component analysis
Chun-Hou Zheng1, De-Shuang Huang, Xiang-Zhen Kong
1College of Information and Communication Technology, Qufu Normal University, Rizhao 276826, China.
Abstract:
We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis (ICA). Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.
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