TY - JOUR T1 - Assessing the measurement transfer function of single-cell RNA sequencing JF - bioRxiv DO - 10.1101/045450 SP - 045450 AU - Hannah R. Dueck AU - Rizi Ai AU - Adrian Camarena AU - Bo Ding AU - Reymundo Dominguez AU - Oleg V. Evgrafov AU - Jian-Bing Fan AU - Stephen A. Fisher AU - Jennifer S. Hernstein AU - Tae Kyung Kim AU - Jae Mun (Hugo) Kim AU - Ming-Yi Lin AU - Rui Liu AU - William J. Mack AU - Sean McGroty AU - Joseph Nguyen AU - Neeraj Salathia AU - Jamie Shallcross AU - Tade Souaiaia AU - Jennifer Spaethling AU - Chris P. Walker AU - Jinhui Wang AU - Kai Wang AU - Wei Wang AU - Andre Wilberg AU - Lina Zheng AU - Robert H. Chow AU - James Eberwine AU - James A. Knowles AU - Kun Zhang AU - Junhyoung Kim Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/03/24/045450.abstract N2 - 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. ER -