Sparse gene expression data analysis method based on truncated power

Abstract

The invention discloses a sparse gene expression data analysis method based on the truncated power. The method specifically includes the steps that a gene data set is preprocessed, namely, the gene data set is regularized, the number of principal components is determined according to a principal component analysis method, and the cardinal number of the principal components is determined in the combination of local iteration; features of gene data in the gene data set processed through the first step are extracted, interference of the data is reduced, and the clustering accuracy in the following procedure is improved; the gene data with the extracted data features are processed according to a clustering method; a clustering processing result acquired through the third step is compared with the set clustering accuracy, adjustment and optimization parameters of sparse dimensionality reduction are fed back and adjusted, and therefore the optimal clustering accuracy is achieved. The method achieves the purpose of sparse feature value decomposition and is used for sparse principal component analysis, the principal component explaining capacity is high, the operation speed is high, a sparse principal component method can be well verified, and the efficiency and the accuracy of gene data analysis are improved.

Description

technical field [0001] The invention discloses a sparse gene expression data analysis method based on truncated power, and relates to the technical field of gene expression data analysis. Background technique [0002] With the rapid development of biomedicine, the wide application of DNA chip (DNA microarray) can quickly measure the expression level of genes. Since the analysis of genetic data can be used to identify cancer cells to predict the probability of a certain disease, it is of great significance to human life. Therefore, gene clustering has become a hot topic in current research. [0003] The original collected genetic data has the characteristics of many attributes and few samples. The results of direct clustering analysis are often disturbed by a large amount of redundant data, and high-dimensional data is also a challenge to traditional clustering methods. In order to overcome these shortcomings, different dimensionality reduction principal feature extraction ...

AI Technical Summary

Problems solved by technology

However, due to the characteristics of genetic data itself, there are many attributes and few samples, so that there will be a large amount of redundant data and interference information in high-dimensional data, and direct clustering analysis will lead to a low accuracy rate.

Principal component analysis is a classic dimensionality reduction method that can map high-dimensional data to low-dimensional space, but its results do not have strong explanatory power

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