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

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.

Shenzhen University researchers have developed a deep learning classifier (scDLC) for large sample scRNA-seq data, based on the long short-term memory recurrent neural networks (LSTMs). Their 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.

Simulation studies show that this 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 framework of scDLC with three layers

Fig. 8

The first layer and the third layer are two fully connected layers, and the middle layer is an LSTMs layer that consists of two long short-term memory network sub layers. A softmax layer is connected at the end to map the output of the classifier to a probability distribution

Availability – The code named “scDLC” is publicly available at

Zhou Y, Peng M, Yang B, Tong T, Zhang B, Tang N. (2022) scDLC: a deep learning framework to classify large sample single-cell RNA-seq data. BMC Genomics 23(1):504. [article]

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