Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. Researchers at the University of Hawaii have developed DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data.
(Sub) Neural network architecture of DeepImpute. Each sub-neural network is composed of four layers. The input layer is genes that are highly correlated with the target genes in the output layer. It is followed by a dense hidden layer of 256 neurons dense layer and a dropout layer (dropout rate = 20%). The output layer consists of a subset of target genes (default N = 512), whose zero values are to be imputed
Availability – DeepImpute is freely available at https://github.com/lanagarmire/DeepImpute