文章基本信息

标题：A reference-free approach for cell type classification with scRNA-seq
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作者：Qi Sun ; Yifan Peng ; Jinze Liu 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2021
卷号：24
期号：8
页码：1-17
DOI：10.1016/j.isci.2021.102855
语种：English
出版社：Elsevier
摘要：SummarySingle-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to characterize cells under different biological conditions. Unlike bulk RNA-seq, gene expression from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that attribute to gene quantification. To overcome data sparsity and fully utilize original reads, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types withk-mer level features. The compressedk-mer groups (CKGs), identified by the simhash method, containk-mers with similar abundance profiles and serve as the cells’ features. Our experiments demonstrate that CKG features lend themselves to better performance than gene expression features in scRNA-seq classification accuracy in the majority of experimental cases. Because CKGs are derived from raw reads without alignment to reference genome, scSimClassify offers an effective alternative to existing methods especially when reference genome is incomplete or insufficient to represent subject genomes.Graphical abstractDisplay OmittedHighlights•Compressedk-mer groups (CKGs) are used to classify cell types without references•CKGs are competitive to gene expression features for cell type classification•CKGs are associated with genes sharing gene specifick-mersBioinformatics; Transcriptomics; Algorithms