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Direct Comparative Analyses of 10X Genomics Chromium and Smart-seq2.
Wang, Xiliang; He, Yao; Zhang, Qiming; Ren, Xianwen; Zhang, Zemin.
Afiliação
  • Wang X; BIOPIC, Beijing Advanced Innovation Center for Genomics, and School of Life Sciences, Peking University, Beijing 100871, China.
  • He Y; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
  • Zhang Q; BIOPIC, Beijing Advanced Innovation Center for Genomics, and School of Life Sciences, Peking University, Beijing 100871, China.
  • Ren X; BIOPIC, Beijing Advanced Innovation Center for Genomics, and School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
  • Zhang Z; BIOPIC, Beijing Advanced Innovation Center for Genomics, and School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China. Electronic address: zemin@pku.edu.cn.
Genomics Proteomics Bioinformatics ; 19(2): 253-266, 2021 04.
Article em En | MEDLINE | ID: mdl-33662621
ABSTRACT
Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%-30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perfilação da Expressão Gênica / Análise de Célula Única Idioma: En Revista: Genomics Proteomics Bioinformatics Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perfilação da Expressão Gênica / Análise de Célula Única Idioma: En Revista: Genomics Proteomics Bioinformatics Ano de publicação: 2021 Tipo de documento: Article