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Sci Rep. 2018 Nov 5;8(1):16329. doi: 10.1038/s41598-018-34688-x.

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data.

Author information

1
Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, India.
2
Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, India. debarka@iiitd.ac.in.
3
Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, India. debarka@iiitd.ac.in.
4
Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology, Delhi, India.

Abstract

The emergence of single-cell RNA sequencing (scRNA-seq) technologies has enabled us to measure the expression levels of thousands of genes at single-cell resolution. However, insufficient quantities of starting RNA in the individual cells cause significant dropout events, introducing a large number of zero counts in the expression matrix. To circumvent this, we developed an autoencoder-based sparse gene expression matrix imputation method. AutoImpute, which learns the inherent distribution of the input scRNA-seq data and imputes the missing values accordingly with minimal modification to the biologically silent genes. When tested on real scRNA-seq datasets, AutoImpute performed competitively wrt., the existing single-cell imputation methods, on the grounds of expression recovery from subsampled data, cell-clustering accuracy, variance stabilization and cell-type separability.

PMID:
30397240
PMCID:
PMC6218547
DOI:
10.1038/s41598-018-34688-x
[Indexed for MEDLINE]
Free PMC Article

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