NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering
AffiliationInterdisciplinary Program in Statistics and Data Science, University of Arizona
Department of Biosystems Engineering, University of Arizona
Department of Electrical and Computer Engineering, University of Arizona
College of Pharmacy, University of Arizona
Department of Biostatistics and Epidemiology, University of Arizona
MetadataShow full item record
PublisherFrontiers Media S.A.
CitationZhang, X., Chen, Z., Bhadani, R., Cao, S., Lu, M., Lytal, N., Chen, Y., & An, L. (2022). NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering. Frontiers in Genetics, 13.
JournalFrontiers in Genetics
RightsCopyright © 2022 Zhang, Chen, Bhadani, Cao, Lu, Lytal, Chen and An. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractSingle-cell RNA sequencing (scRNA-seq) reveals the transcriptome diversity in heterogeneous cell populations as it allows researchers to study gene expression at single-cell resolution. The latest advances in scRNA-seq technology have made it possible to profile tens of thousands of individual cells simultaneously. However, the technology also increases the number of missing values, i. e, dropouts, from technical constraints, such as amplification failure during the reverse transcription step. The resulting sparsity of scRNA-seq count data can be very high, with greater than 90% of data entries being zeros, which becomes an obstacle for clustering cell types. Current imputation methods are not robust in the case of high sparsity. In this study, we develop a Neural Network-based Imputation for scRNA-seq count data, NISC. It uses autoencoder, coupled with a weighted loss function and regularization, to correct the dropouts in scRNA-seq count data. A systematic evaluation shows that NISC is an effective imputation approach for handling sparse scRNA-seq count data, and its performance surpasses existing imputation methods in cell type identification. Copyright © 2022 Zhang, Chen, Bhadani, Cao, Lu, Lytal, Chen and An.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as Copyright © 2022 Zhang, Chen, Bhadani, Cao, Lu, Lytal, Chen and An. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).