ArticleNAR genomics and bioinformatics2020
SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Sensitivity analysis of cell fate trajectories from single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.PLoS computational biology · 2025Article
- Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.Scientific reports · 2025Article
- Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.Cell reports methods · 2024Article
- DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.Genome biology · 2023Article
- SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.PLoS computational biology · 2021Article
- AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders.Frontiers in genetics · 2021Article
- A Review of Integrative Imputation for Multi-Omics Datasets.Frontiers in genetics · 2020Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called 'dropout'), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.