RT Journal Article
SR Electronic
T1 Power Analysis of Single Cell RNA-Sequencing Experiments
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 073692
DO 10.1101/073692
A1 Valentine Svensson
A1 Kedar Nath Natarajan
A1 Lam-Ha Ly
A1 Ricardo J Miragaia
A1 Charlotte Labalette
A1 Iain C Macaulay
A1 Ana Cvejic
A1 Sarah A Teichmann
YR 2016
UL http://biorxiv.org/content/early/2016/09/08/073692.abstract
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.