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

Author:

Tang Yachen1,Li Xuefeng1,Shi Mingguang1

Affiliation:

1. Hefei University of Technology, Hefei, China

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 to learn encoder-decoder structures for identifying cell embedding. Results LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.

Funder

National Natural Science Foundation of China

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference38 articles.

1. NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks;Ahsan;Genome Biology,2021

2. SCENIC: single-cell regulatory network inference and clustering;Aibar;Nature Methods,2017

3. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells;Buettner;Nature Biotechnology,2015

4. Chromatin Interaction Neural Network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences;Cao;bioRxiv,2021

5. Constructing cell lineages from single-cell transcriptomes;Chen;Molecular Aspects of Medicine,2018

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