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首页> 外文期刊>BMC Genomics >Single cell RNA-seq data clustering using TF-IDF based methods
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Single cell RNA-seq data clustering using TF-IDF based methods

机译：使用基于TF-IDF的方法的单个单元RNA-SEQ数据聚类

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摘要

Single cell transcriptomics is critical for understanding cellular heterogeneity and identification of novel cell types. Leveraging the recent advances in single cell RNA sequencing (scRNA-Seq) technology requires novel unsupervised clustering algorithms that are robust to high levels of technical and biological noise and scale to datasets of millions of cells. We present novel computational approaches for clustering scRNA-seq data based on the Term Frequency - Inverse Document Frequency (TF-IDF) transformation that has been successfully used in the field of text analysis. Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.
机译：单细胞转录组学对于了解细胞异质性和新细胞类型的鉴定至关重要。利用单细胞RNA测序（SCRNA-SEQ）技术的最新进展需要新颖的无监督聚类算法，其对高水平的技术和生物噪声和数百万细胞的数据集进行稳健。我们提出了基于在文本分析领域中成功使用的术语频率 - 逆文档频率（TF-IDF）转换来聚类SCRNA-SEQ数据的新型计算方法。经验实验结果表明，TF-IDF方法始终如一地优于常用的ScrNA-SEQ聚类方法。

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