scDLC: a deep learning framework to classify large sample single-cell RNA-seq data

Yan Zhou, Minjiao Peng, Bin Yang, Tiejun Tong, Baoxue Zhang, Niansheng Tang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)

Abstract

Background: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated. Results: We propose a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Our new scDLC does not require a prior knowledge on the data distribution, but instead, it takes into account the dependency of the most outstanding feature genes in the LSTMs model. LSTMs is a special recurrent neural network, which can learn long-term dependencies of a sequence. Conclusions: Simulation studies show that our new scDLC performs consistently better than the existing methods in a wide range of settings with large sample sizes. Four real scRNA-seq datasets are also analyzed, and they coincide with the simulation results that our new scDLC always performs the best. The code named “scDLC” is publicly available at https://github.com/scDLC-code/code.

Original languageEnglish
Article number504
JournalBMC Genomics
Volume23
Issue number1
Early online date12 Jul 2022
DOIs
Publication statusPublished - Dec 2022

Scopus Subject Areas

  • Biotechnology
  • Genetics

User-Defined Keywords

  • Classifier
  • Deep learning
  • Single-cell RNA sequencing

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