基于单细胞RNA-Seq测序数据的聚类分析
摘要：随着生物技术的发展，人们越来越重视单细胞测序技术。单细胞测序技术越来越多被应用在各种科研领域。2015年以来，10X Genomics、Drop-seq、Micro-well、Split-seq等技术的出现，彻底降低了单细胞测序的成本门槛。自此，单细胞测序技术被广泛应用于基础科研和临床研究。现在单细胞测序技术在许多领域都有重要的地位。在癌症早期的诊断、追踪以及个体化治疗，免疫细胞衰老，早期胚胎发育和干细胞研究，等具有极其重要的意义。随着单细胞转录组测序技术的不断发展，各技术环节的日臻完善，其必将在医疗卫生等领域取得更加丰硕的成果，成为人们治疗疾病、探索生命科学的一项必不可少的技术。
单细胞测序数据通常会涉及许多细胞，而每个细胞中的基因数量可能是几万个，所以单细胞测序数据是极其复杂的数据。通过无监督聚类分析可以更好的区分不同的细胞。为后续的研究带来了极大的便利。通过聚类分析出来不同组别的细胞可以较为轻松的进行差异识别，而不是在大量的数据中进行冗杂的对比。
关键词：单细胞测序技术 聚类分析 无监督学习
THEME：Cluster analysis based on single cell RNA-Seq sequencing data
Abstract: With the development of biotechnology, people pay more and more attention to single cell sequencing technology. Since the advent of single cell sequencing technology, it has been more and more applied in various scientific research fields. Since 2015, the emergence of 10X Genomics, Ddrop-seq, Micro-well, Split-seq and other technologies has completely lowered the cost threshold of single-cell sequencing. Since then, single-cell sequencing technology has been widely used in basic research and clinical research. Now single cell sequencing technology has an important position in many fields. Early diagnosis, tracking, and individualized treatment of cancer, immune cell senescence, early embryonic development, and stem cell research are of great importance.
With the continuous development of single-cell transcriptome sequencing technology and the improvement of each technical link, it will achieve more fruitful results in medical and health fields, and become an essential technology for people to treat diseases and explore life science.
Single-cell sequencing data typically involves many cells, and the number of genes in each cell can be tens of thousands, so single-cell sequencing data is extremely complex. Unsupervised cluster analysis can be used to better distinguish different cells. It has brought great convenience for the follow-up research. Through cluster analysis, different groups of cells can be easily identified, rather than miscellaneous comparison in a large number of data.
Keywords：single cell sequencing clustering analysis unsupervised learning
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