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Activity Number: 179 - Statistical Methods in Single-Cell Transcriptomics
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #313417
Title: NISC: Accurate Clustering Through Neural Network-Imputation for Single-Cell RNA-Seq Data with High Sparsity
Author(s): Xiang Zhang* and Siyang Cao and Meng Lu and Nicholas Lytal and Lingling An
Companies: University of Arizona and Department of Electrical & Computer Engineering, University of Arizona and Interdisciplinary Program in Statistics and Data Science, University of Arizona and Interdisciplinary Program in Statistics and Data Science, University of Arizona and Interdisciplinary Program in Statistics and Data Science, University of Arizona
Keywords: Single-cell; High sparsity; Dropout; Imputation; Neural Network; Autoencoder
Abstract:

High dimensional single-cell RNA sequencing (scRNA-seq) data become available recently to describe gene expression of tens of thousands of genes over increasing volume of single cells. However, this technology raises numbers of missing values (referred “dropouts”) caused by failures in amplification during the reverse-transcription of sequencing or other sources. The scRNA-seq data can reach more than 90% sparsity. Most existing imputation methods cannot handle the data with extremely high sparsity. We propose NISC (neural network-based imputation for single-cell RNA-seq data), an imputation approach that uses autoencoder with a novel loss function to recognize and correct the dropouts in scRNA-seq data. A series of comprehensive experiments show that NISC outperforms the available imputation methods on highly sparse scRNA-seq data.


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