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)


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

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

Scopus Subject Areas

  • Biotechnology
  • Genetics

User-Defined Keywords

  • Classifier
  • Deep learning
  • Single-cell RNA sequencing


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