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

被引：0
作者：
Cédric Arisdakessian
Olivier Poirion
Breck Yunits
Xun Zhu
Lana X. Garmire
机构：
[1] University of Hawaii at Manoa,Department of Information and Computer Science
[2] University of Hawaii Cancer Center,Department of Epidemiology
[3] University of Hawaii at Manoa,Department of Molecular Biology and Bioengineering
[4] University of Michigan,Department of Computational Medicine and Bioinformatics
来源：
Genome Biology | / 20卷
关键词：
RNA-seq; Single-cell; Imputation; Deep learning; Machine learning; Neural network; Dropout; DeepImpute;
D O I：
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中图分类号：
学科分类号：
摘要：
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.
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[1]  
Usoskin D(2015)Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing Nat Neurosci 18 145-1142
[2]  
Furlan A(2017)Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors Science 356 eaah4573-779
[3]  
Islam S(2015)Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq Science 347 1138-478
[4]  
Abdo H(2014)Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types Science 343 776-620
[5]  
Lönnerberg P(2014)Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq Nature 509 371-10988
[6]  
Lou D(2016)Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma Nature 539 309-29.e27
[7]  
Villani A-C(2013)Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells Nature 498 236-542
[8]  
Satija R(2010)Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis Cell Stem Cell 6 468-347
[9]  
Reynolds G(2015)Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression Nat Commun 6 8687-1259
[10]  
Sarkizova S(2015)The technology and biology of single-cell RNA sequencing Mol Cell 58 610-71.e6
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