DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing fo...

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Bibliographic Details
Published in:Genome Biology 2019-10, Vol.20 (1), p.211-211, Article 211
Main Authors: Arisdakessian, Cédric, Poirion, Olivier, Yunits, Breck, Zhu, Xun, Garmire, Lana X
Format: Article
Language:English
Subjects:
Advantages
Algorithms
Bioinformatics
Datasets
Deep learning
Gene expression
genome
Genomics
Genomics - methods
Imputation
Machine Learning
Method
Methods
Neural network
Neural networks
Neural Networks, Computer
Peer review
Ribonucleic acid
RNA
RNA-seq
School dropouts
sequence analysis
Sequence Analysis, RNA
Single-cell
Single-Cell Analysis
Software
Statistical analysis
Values
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Summary:Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present 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, and is freely available at https://github.com/lanagarmire/DeepImpute .
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1837-6