Assessing the measurement transfer function of single-cell RNA sequencing
By
Hannah R. Dueck,
Rizi Ai,
Adrian Camarena,
Bo Ding,
Reymundo Dominguez,
Oleg V Evgrafov,
Jian-Bing Fan,
Stephen A Fisher,
Jennifer S. Hernstein,
Tae Kyung Kim,
Jae Mun (Hugo) Kim,
Ming-Yi Lin,
Rui Liu,
William J. Mack,
Sean McGroty,
Joseph Nguyen,
Neeraj Salathia,
Jamie Shallcross,
Tade Souaiaia,
Jennifer Spaethling,
Chris P. Walker,
Jinhui Wang,
Kai Wang,
Wei Wang,
Andre Wilberg,
Lina Zheng,
Robert H Chow,
James Eberwine,
James A Knowles,
Kun Zhang,
Junhyoung Kim
Posted 24 Mar 2016
bioRxiv DOI: 10.1101/045450
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.
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