Abstract
In the past two or three years, genome sequencing technology has been rapidly developed. Large-scale sequencing projects such as the Human Genome Project and the Cancer Genome Project have been launched one after another. Up to now, due to the emergence and research of artificial intelligence, it has brought us many possibilities. The purpose of this article is to use artificial intelligence to help single-cell transcription sequencing as much as possible. Based on the idea of Euclid algorithm, an improved K-means algorithm is proposed, which to a certain extent avoids the phenomenon of clustering results falling into local solutions, and reduces the appearance of the original K-means algorithm due to the use of error squares criterion function. In the case of dividing large clusters, the simulation experiment results show that the improved K-means algorithm is better than the original algorithm and has better stability.
Highlights
Due to the reprogramming of genome and epigenome and DNA replication errors during cell division and differentiation, different genomes, transcriptome and epigenome will appear at the single cell level [1]
The development and improvement of single cell transcriptome sequencing technology can be used to detect cell heterogeneity and transcriptome analysis of trace samples, so as to understand the complexity of eukaryotic transcriptome genes, single nucleotide polymorphism (SNP), copy number variation of single cell genome, genomic structure variation of single cell genome, gene expression level and gene fusion, Alternative splicing and DNA methylation status contribute to a comprehensive understanding of disease and life processes [2]
It is difficult to separate, many kinds of single cell separation techniques can meet the requirements of isolation and extraction of different types of cells, and obtain the complete transcription expression profile at the single cell level, so as to obtain the ideal single cell to the maximum extent
Summary
Due to the reprogramming of genome and epigenome and DNA replication errors during cell division and differentiation, different genomes, transcriptome and epigenome will appear at the single cell level [1]. The commonly used single cell separation techniques include microfluidic method, continuous dilution method, micromanipulation method, fluorescence activated cell sorting method and laser capture microdissection method [3]. This is a conventional method used before. With the continuous improvement and development of the second and third generation sequencing technology, single cell transcriptome sequencing technology has been widely used in various fields such as tumor genome project and human genome project [4,5] These sequencing projects use conventional transcriptome analysis methods to analyze the mixed samples of millions of cells. The results are the average values obtained by a large number of cell sequencing analysis, or reflect the main cell data, but ignore the differences in gene expression among heterogeneous single cells, which is not conducive to the tracking of cellular pathological process and the study of biodiversity [6]
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