DOI: 10.1101/504977Dec 29, 2018Paper

Imputation of single-cell gene expression with an autoencoder neural network

BioRxiv : the Preprint Server for Biology
Md. Bahadur BadshaAudrey Qiuyan Fu

Abstract

Background: Single-cell RNA-sequencing (scRNA-seq) is a rapidly evolving technology that enables measurement of gene expression levels at an unprecedented resolution. Despite the explosive growth in the number of cells that can be assayed by a single experiment, scRNA-seq still has several limitations, including high rates of dropouts, which result in a large number of genes having zero read count in the scRNA-seq data, and complicate downstream analyses. Methods: To overcome this problem, we treat zeros as missing values and develop nonparametric deep learning methods for imputation. Specifically, our LATE (Learning with AuToEncoder) method trains an autoencoder with random initial values of the parameters, whereas our TRANSLATE (TRANSfer learning with LATE) method further allows for the use of a reference gene expression data set to provide LATE with an initial set of parameter estimates. Results: On both simulated and real data, LATE and TRANSLATE outperform existing scRNA-seq imputation methods, achieving lower mean squared error in most cases, recovering nonlinear gene-gene relationships, and better separating cell types. They are also highly scalable and can efficiently process over 1 million cells in just a few hours on ...Continue Reading

Related Concepts

Gene Expression
Genes
Learning
Computer Software
Learning and Learning Problems
Learning stimulant
Structure
Research Study
Sequence Determinations, RNA
Analysis

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