BMC Genomics (Jul 2022)

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

DOI
https://doi.org/10.1186/s12864-022-08715-1
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 12

Abstract

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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 .

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