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SAVER: Gene expression recovery for UMI-based single cell RNA sequencing

https://doi.org/10.1101/138677
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Multiple Imputation for Nonresponse in Surveys
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Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms
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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
inferredcites →https://doi.org/10.1016/j.cell.2016.09.027openalex-snapshot
Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells
inferredcites →https://doi.org/10.1016/j.cell.2018.05.061openalex-snapshot
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
inferredcites →https://doi.org/10.1016/j.celrep.2017.03.004openalex-snapshot
Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
inferredcites →https://doi.org/10.1016/j.cels.2016.08.011openalex-snapshot
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
inferredcites →https://doi.org/10.1016/j.cels.2018.01.014openalex-snapshot
Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH
inferredcites →https://doi.org/10.1016/j.molcel.2015.03.005openalex-snapshot
Single Mammalian Cells Compensate for Differences in Cellular Volume and DNA Copy Number through Independent Global Transcriptional Mechanisms
inferredcites →https://doi.org/10.1038/nature22794openalex-snapshot
Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance
inferredcites →https://doi.org/10.1038/nbt.2642openalex-snapshot
Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments
inferredcites →https://doi.org/10.1038/nbt.3192openalex-snapshot
Spatial reconstruction of single-cell gene expression data
inferredcites →https://doi.org/10.1038/nmeth.2967openalex-snapshot
Bayesian approach to single-cell differential expression analysis
inferredcites →https://doi.org/10.1038/nmeth.4220openalex-snapshot
Power analysis of single-cell RNA-sequencing experiments
inferredcites →https://doi.org/10.1038/nmeth.4263openalex-snapshot
SCnorm: robust normalization of single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/s41467-018-03405-7openalex-snapshot
An accurate and robust imputation method scImpute for single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/s41593-017-0029-5openalex-snapshot
Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
inferredcites →https://doi.org/10.1073/pnas.1721085115openalex-snapshot
Gene expression distribution deconvolution in single-cell RNA sequencing
inferredcites →https://doi.org/10.1093/bioinformatics/17.6.520openalex-snapshot
Missing value estimation methods for DNA microarrays
inferredcites →https://doi.org/10.1093/bioinformatics/btr597openalex-snapshot
MissForest—non-parametric missing value imputation for mixed-type data
inferredcites →https://doi.org/10.1101/217737openalex-snapshot
K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data
inferredcites →https://doi.org/10.1109/vetecs.2005.1543265openalex-snapshot
Correlation Matrix Distance, a Meaningful Measure for Evaluation of Non-Stationary MIMO Channels
inferredcites →https://doi.org/10.1126/science.aaa1934openalex-snapshot
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
inferredcites →https://doi.org/10.1186/s13059-015-0844-5openalex-snapshot
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
inferredcites →https://doi.org/10.1186/s13059-016-0930-3openalex-snapshot
Beyond comparisons of means: understanding changes in gene expression at the single-cell level
inferredcites →https://doi.org/10.1186/s13059-016-0947-7openalex-snapshot
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
inferredcites →https://doi.org/10.1186/s13059-016-1010-4openalex-snapshot
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
inferredcites →https://doi.org/10.1186/s13059-016-1077-yopenalex-snapshot
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
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Stochastic mRNA Synthesis in Mammalian Cells
inferredcites →https://doi.org/10.1371/journal.pcbi.1004333openalex-snapshot
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
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Regularization Paths for Generalized Linear Models via Coordinate Descent
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Spectral Regularization Algorithms for Learning Large Incomplete Matrices

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-1-4939-9240-9_26openalex-snapshot
A Bioinformatic Toolkit for Single-Cell mRNA Analysis
inferred← citeshttps://doi.org/10.1016/b978-0-12-814919-5.00019-1openalex-snapshot
Data Analysis in Single-Cell RNA-Seq
inferred← citeshttps://doi.org/10.1016/j.cell.2018.03.041openalex-snapshot
Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing
inferred← citeshttps://doi.org/10.1016/j.cels.2018.01.014openalex-snapshot
Rare Cell Detection by Single-Cell RNA Sequencing as Guided by Single-Molecule RNA FISH
inferred← citeshttps://doi.org/10.1016/j.coisb.2017.12.008openalex-snapshot
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105578openalex-snapshot
DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data
inferred← citeshttps://doi.org/10.1016/j.devcel.2021.07.017openalex-snapshot
Spatial Transcriptional Mapping of the Human Nephrogenic Program
inferred← citeshttps://doi.org/10.1038/s41467-018-03405-7openalex-snapshot
An accurate and robust imputation method scImpute for single-cell RNA-seq data
inferred← citeshttps://doi.org/10.1038/s41467-018-07931-2openalex-snapshot
Single-cell RNA-seq denoising using a deep count autoencoder
inferred← citeshttps://doi.org/10.1038/s41588-018-0089-9openalex-snapshot
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
inferred← citeshttps://doi.org/10.1038/s41591-018-0078-7openalex-snapshot
Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis
inferred← citeshttps://doi.org/10.1093/bioinformatics/btaa278openalex-snapshot
PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing
inferred← citeshttps://doi.org/10.1093/bioinformatics/btz726openalex-snapshot
bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.1101/2022.06.09.495525openalex-snapshot
Diffusion on PCA-UMAP manifold captures a well-balance of local, global, and continuum structure to denoise single-cell RNA sequencing data
inferred← citeshttps://doi.org/10.1101/433102openalex-snapshot
scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods
inferred← citeshttps://doi.org/10.1101/gr.251603.119openalex-snapshot
netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis
inferred← citeshttps://doi.org/10.1109/tcbb.2018.2848633openalex-snapshot
Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
inferred← citeshttps://doi.org/10.1109/tcbb.2019.2893170openalex-snapshot
(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices
inferred← citeshttps://doi.org/10.1186/s12859-025-06147-8openalex-snapshot
scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data
inferred← citeshttps://doi.org/10.1186/s12864-019-5747-5openalex-snapshot
scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data
inferred← citeshttps://doi.org/10.1186/s13059-017-1305-0openalex-snapshot
Splatter: simulation of single-cell RNA sequencing data
inferred← citeshttps://doi.org/10.1186/s13059-019-1812-2openalex-snapshot
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions
inferred← citeshttps://doi.org/10.1186/s13073-017-0492-3openalex-snapshot
Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists
inferred← citeshttps://doi.org/10.1214/17-aoas1110openalex-snapshot
A unified statistical framework for single cell and bulk RNA sequencing data
inferred← citeshttps://doi.org/10.12688/f1000research.15809.1openalex-snapshot
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010129openalex-snapshot
HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data
inferred← citeshttps://doi.org/10.33612/diss.255266474openalex-snapshot
Context matters
inferred← citeshttps://doi.org/10.3390/biology14070743openalex-snapshot
scQTLtools: An R/Bioconductor Package for Comprehensive Identification and Visualization of Single-Cell eQTLs
inferred← citeshttps://doi.org/10.64898/2026.07.30.741939openalex-snapshot
Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional states
inferred← citeshttps://doi.org/10.70675/0e631420z8535z4e89zb698z98ce72f6f5b7openalex-snapshot
Causal network analysis of single cell multi-omics data applied to a cellular therapy against multiple sclerosis
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