Abstract
Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.
Highlights
Singe-cell RNA sequencing has emerged as a state-of-the-art technique for transcriptome analysis
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We developed G2S3, an imputation method that recovers gene expression in scRNA-seq data by borrowing information from adjacent genes in a gene graph learned by graph signal processing
Summary
Singe-cell RNA sequencing (scRNA-seq) has emerged as a state-of-the-art technique for transcriptome analysis. Compared to bulk RNA-seq that measures the average gene expression. Gene graph-based imputation for single-cell transcriptomics published data. The detailed list of data sets used in the study is described in the “Real datasets” section. The code to reproduce all the analyses presented in the paper are available on GitHub https://github.com/ZWang-Lab/G2S3_paper2020
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