Comparative Analysis of Single-Cell RNA Sequencing Methods

Mol Cell. 2017 Feb 16;65(4):631-643.e4. doi: 10.1016/j.molcel.2017.01.023.


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.

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

Publication types

  • Comparative Study

MeSH terms

  • Animals
  • Base Sequence
  • Cell Line
  • Computer Simulation
  • Cost-Benefit Analysis
  • Embryonic Stem Cells / chemistry*
  • High-Throughput Nucleotide Sequencing* / economics
  • Mice
  • Models, Economic
  • RNA / genetics*
  • RNA / isolation & purification
  • Sequence Analysis, RNA / economics
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / economics
  • Single-Cell Analysis / methods*


  • RNA