Feature selection for single cell RNA sequencing data based on a noise-robust fuzzy relation and fuzzy evidence theory

Abstract

Feature selection plays an important role in removing noise and redundancy of data. Due to various uncertainties in single cell RNA sequencing (scRNA-seq) data, such as noise, mutation, sparsity, etc., most of the existing feature selection methods for scRNA-seq data performed poorly. This paper has given some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a noise-robust fuzzy relation incorporating the decision attribute is defined because the decision attribute contains the most important information of data. Then, fuzzy evidence theory is introduced because it is good at handling the uncertainty of continuous data. Furthermore, the connection between fuzzy evidence theory and fuzzy rough set models has been established, which solves the problem of difficult calculation of fuzzy belief and fuzzy plausibility. In this framework, two noise-robust feature selection algorithms without any parameters for scRNA-seq data are proposed. Experiments on 12 scRNA-seq datasets show that the proposed algorithms are superior to the other four state-of-the-art algorithms in classification accuracy and noise-robust performance in the case of selecting fewer genes. Therefore, the proposed algorithms are noise-robust and objective while maintaining high gene selection efficiency and accuracy.