System and method for clustering gene expression data based on manifold learning

Abstract

The invention discloses a method for clustering gene expression data based on manifold learning, and the method provided by the invention comprises the following steps: acquiring a gene expression data matrix A through an acquisition system, and preprocessing the gene expression data matrix A by using a local linear smoothing algorithm; introducing the preprocessed data matrix A, and constructing a weighted neighborhood figure G in a three-dimensional space; taking the shortest path between two points as the approximate geodesic distance between two points; calculating a two-dimensional embedded coordinate by using an MDS (minimum discernible signal), and mapping the three-dimensional data matrix A to a two-dimensional visual space; and carrying out clustering on the two-dimensional visual space subjected to mapping by using a k-mean clustering algorithm so as to obtain the clustering result. The clustering method has the characteristics of low calculating cost, capability of eliminating high-order redundancies, suitability for pattern classification tasks, and the like; and by using the method disclosed by the invention, the current states of cells, the effectiveness of medicaments to malignant cells, and the like can be discriminated effectively according to the clustering result. The invention also provides a system for clustering gene expression data based on manifold learning.

Description

technical field [0001] The invention belongs to the field of data mining and processing, and in particular relates to a clustering system and method of gene expression data based on manifold learning. Background technique [0002] The progress of science, especially the rapid development of bioinformatics, has brought us into a new era. As one of the core and frontier fields of life sciences and natural sciences, human beings still know little about the mysteries of gene sequences. Bioinformatics and related content still have a long way to go. High-throughput microarray detection technology is a significant breakthrough in biological experiment technology in recent years. With this technology, the transcription levels of thousands of genes can be analyzed in parallel at the same time. Data from large-scale gene expression experiments. Due to the variety of cells and the temporal and spatial specificity of gene expression, the gene expression data is more complex, the data...

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Problems solved by technology

[0006] In the above gene expression data clustering algorithms, the traditional clustering algorithm clusters from a single direction and cannot discover the local information of the data; the double clustering algorithm can effectively mine the local information, but it cannot be applied to higher-dimensional gene expression data; and the manifold learning algorithm is a relatively new technology with a very broad application prospect. In comparison, it can overcome the influence of high-dimensional features of gene expression data

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