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Comparative analysis of single-cell RNA sequencing methods

GSE75790 Mus musculus Expression profiling by high throughput sequencing; Third-party reanalysis 583 samples Submitted 2016/09/09 Platform GPL17021Platform GPL13112Platform GPL18480
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 provides a framework for benchmarking further improvements of scRNA-seq protocols.
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Direct links to NCBI, no account and no request form: the whole study as GSE75790_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 583 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA305381 and SRA study SRP067124. Searching any of these in the dataset finder brings you back here.

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