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Molecular cell v.65 no.4, 2017년, pp.631 - 643.e4   SCI SCIE
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Comparative Analysis of Single-Cell RNA Sequencing Methods

Ziegenhain, Christoph (Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany ) ; Vieth, Beate (Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany ) ; Parekh, Swati (Anthropology & Human Genomics, Department of Biology II, Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany ) ; Reinius, Björn (Ludwig Institute for Cancer Research, Box 240, 171 77 Stockholm, Sweden ) ; Guillaumet-Adkins, Amy (CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain ) ; Smets, Martha (Department of Biology II and Center for Integrated Protein Science Munich (CIPSM), Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany ) ; Leonhardt, Heinrich (Department of Biology II and Center for Integrated Protein Science Munich (CIPSM), Ludwig-Maximilians University, Großhaderner Straße 2, 82152 Martinsried, Germany ) ; Heyn, Holger (CNAG-CRG, Centre for Genomic ) ; Hellmann, Ines ; Enard, Wolfgang ;
  • 초록  

    Summary Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols. Highlights The study represents the most comprehensive comparison of scRNA-seq protocols Power simulations quantify the effect of sensitivity and precision on cost efficiency The study offers an informed choice among six prominent scRNA-seq methods The study provides a framework for benchmarking future protocol improvements Graphical Abstract [DISPLAY OMISSION]


  • 주제어

    single-cell RNA-seq .   method comparison .   transcriptomics .   power analysis .   simulation .   cost-effectiveness.  

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