RT Journal Article
SR Electronic
T1 SuperCT: A supervised-learning-framework to enhance the characterization of single-cell transcriptomic profiles
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 416719
DO 10.1101/416719
A1 Peng Xie
A1 Mingxuan Gao
A1 Chunming Wang
A1 Pawan Noel
A1 Chaoyong Yang
A1 Daniel Von Hoff
A1 Haiyong Han
A1 Michael Q. Zhang
A1 Wei Lin
YR 2018
UL http://biorxiv.org/content/early/2018/09/16/416719.1.abstract
AB Characterization of individual cell types is fundamental to the study of multicellular samples such as tumor tissues. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering of cells followed by visualization of clusters in a low-dimensional space. Clusters are often assigned to different cell types based on canonical markers. However, the efficiency of characterizing the known cell types in this way is low and limited by the investigator[s] knowledge. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities based on their RNA expression profiles. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.