LIDER: cell embedding based deep neural network classifier for supervised cell type identification.

ABSTRACT:

Background

Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation.

Methods

Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder

SUBMITTER: Tang Y 

PROVIDER: S-EPMC10439717 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature