PT  - JOURNAL ARTICLE
AU  - Valentine Svensson
AU  - Kedar Nath Natarajan
AU  - Lam-Ha Ly
AU  - Ricardo J Miragaia
AU  - Charlotte Labalette
AU  - Iain C Macaulay
AU  - Ana Cvejic
AU  - Sarah A Teichmann
TI  - Power Analysis of Single Cell RNA-Sequencing Experiments
AID  - 10.1101/073692
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 073692
4099  - http://biorxiv.org/content/early/2016/09/08/073692.short
4100  - http://biorxiv.org/content/early/2016/09/08/073692.full
AB  - High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.