RT Journal Article
SR Electronic
T1 Assessing the measurement transfer function of single-cell RNA sequencing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 045450
DO 10.1101/045450
A1 Hannah R. Dueck
A1 Rizi Ai
A1 Adrian Camarena
A1 Bo Ding
A1 Reymundo Dominguez
A1 Oleg V. Evgrafov
A1 Jian-Bing Fan
A1 Stephen A. Fisher
A1 Jennifer S. Hernstein
A1 Tae Kyung Kim
A1 Jae Mun (Hugo) Kim
A1 Ming-Yi Lin
A1 Rui Liu
A1 William J. Mack
A1 Sean McGroty
A1 Joseph Nguyen
A1 Neeraj Salathia
A1 Jamie Shallcross
A1 Tade Souaiaia
A1 Jennifer Spaethling
A1 Chris P. Walker
A1 Jinhui Wang
A1 Kai Wang
A1 Wei Wang
A1 Andre Wilberg
A1 Lina Zheng
A1 Robert H. Chow
A1 James Eberwine
A1 James A. Knowles
A1 Kun Zhang
A1 Junhyoung Kim
YR 2016
UL http://biorxiv.org/content/early/2016/03/24/045450.abstract
AB Recently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.