RT Journal Article
SR Electronic
T1 scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 506642
DO 10.1101/506642
A1 Lifei Wang
A1 Rui Nie
A1 Ruyue Xin
A1 Jiang Zhang
A1 Jun Cai
YR 2018
UL http://biorxiv.org/content/early/2018/12/27/506642.abstract
AB Recent advances in single cell RNA sequencing (scRNA-seq) call more computational analysis methods. As the data for non-characterized cells accumulates quickly, supervised learning model is an ideal tool to classify the non-characterized cells based on the previously well characterized cells. However, deep learning model is an appropriate tool to deal with vast and complex data such as RNA-seq data, but lacks of interpretability. Here for the first time, we present scCapsNet, a deep learning model adapted from CapsNet. The scCapsNet model retains the capsule parts of CapsNet and replaces the part of convolutional neural networks with several parallel fully connected neural networks. We apply scCapsNet to scRNA-seq data of mouse retinal bipolar cells and human peripheral blood mononuclear cells (PBMC). The results show that scCapsNet performs well as a classifier. Meanwhile, the results also demonstrate that the parallel fully connected neural networks function like feature detectors as we supposed. The scCapsNet model provides precise contribution of each extracted feature to the cell type recognition. Furthermore, we mix the RNA expression of two cells with different cell types and then use the scCapsNet model trained with non-mixed data to predict the cell types in the mixed data. Our scCapsNet model could predict cell types in a cell mixture with high accuracy.