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

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Talwar D,Mongia A,Sengupta D,Majumdar A

doi

10.1038/s41598-018-34688-x

subject

Has Abstract

pub_date

2018-11-05 00:00:00

pages

16329

issue

1

issn

2045-2322

pii

10.1038/s41598-018-34688-x

journal_volume

8

pub_type

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