SAVER: Gene expression recovery for single-cell RNA sequencing

By Mo Huang, Jingshu Wang, E. Torre, Hannah Dueck, Sydney M. Shaffer, R. Bonasio, J. Murray, Arjun Raj, Mingyao Li, Nancy R. Zhang. Nature Methods, 2018

673 citations recorded by the source corpus.

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes. SAVER accurately recovers expression values in single-cell RNA-sequencing data to improve downstream analysis.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9242f3b653cd58f5a79cfa733a63fc38bc5da195

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Citations in the corpus

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, neutral): “For example, imputation uses the average gene expression profiles of transcriptionally similar cells to infer missing values caused by transcript dropout events (Huang et al., 2018; van Dijk et al., 2018).”
Single-cell RNA-seq denoising using a deep count autoencoder (2018, methodology): “SAVER .”
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses (2021, methodology): “The scores are compared between scGNN and nine imputation tools (i.e., MAGIC 4 , SAUCIE 10 , SAVER 19 , scImpute 33 , scVI 32 , DCA 11 , DeepImpute 34 , scIGANs 35 , and netNMF-sc 36 ), using the default parameters.”
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions (2019, methodology): “It has been observed [27, 28] that in some cases imputation tends to enforce spurious proximities between cells, which in turn manifest as artefactual gradients, i.e., discrete states pertaining to be a series of cells gradually modulating expression of certain genes along a temporal process or a…”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “…can increase the depth of sequencing, screen spots and genes with strict cut-off values to reduce the sparsity of the filtered expression matrix, or consider applying imputation algorithms ( f or example, SAVER 68 , MAGIC 69 , and WEDGE 70 ) to impute the zero elements in the expression matrix.”
Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, methodology): “Several methods and tools have been developed recently in this regard, including statistical models CIDR (clustering through imputation and dimensionality reduction) 18 , scImpute 19 , MAGIC 20 and SAVER 21 , to name a few, and deep learning approaches DeepImpute 22 , DCA (deep count autoencoder) 23”