ArticleNucleic acids research2022
scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- scCADI: Cell-Aware Diffusion Model for Single-Cell RNA-seq Imputation.Interdisciplinary sciences, computational life sciences · 2026Article
- Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference.Biology · 2026Review
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- SpaMFG: a spatial multi-omics integration method based on feature grouping.Bioinformatics (Oxford, England) · 2026Article
- iLDA-SGCN: Identifying Associations Between Age-Related Diseases and Long Non-Coding RNAs Using Dual Graph Convolutional Networks.Aging cell · 2026Article
- scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.PLoS computational biology · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis.Small methods · 2026Article
- GDSim: accurate simulation for single-cell transcriptomes based on the guided diffusion model.Briefings in bioinformatics · 2026Article
- Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing.NAR genomics and bioinformatics · 2026Article
- scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation.Briefings in bioinformatics · 2026Article
- TPpred-CMvL: prediction of multi-functional therapeutic peptide using contrast multi-view learning.BMC biology · 2025Article
- SpaMWGDA: Identifying spatial domains of spatial transcriptomes using multi-view weighted fusion graph convolutional network and data augmentation.PLoS computational biology · 2025Article
- Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches.Journal of translational medicine · 2025Article
- OmniCellX: A Versatile and Comprehensive Browser-Based Tool for Single-Cell RNA Sequencing Analysis.Biology · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- scTsI: an effective two-stage imputation method for single-cell RNA-seq data.Briefings in bioinformatics · 2025Article
- An in-depth benchmark framework for evaluating single cell RNA-seq dropout imputation methods and the development of an improved algorithm afMF.Clinical and translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advent of single-cell RNA sequencing (scRNA-seq), one major challenging is the so-called 'dropout' events that distort gene expression and remarkably influence downstream analysis in single-cell transcriptome. To address this issue, much effort has been done and several scRNA-seq imputation methods were developed with two categories: model-based and deep learning-based. However, comprehensively and systematically comparing existing methods are still lacking. In this work, we use six simulated and two real scRNA-seq datasets to comprehensively evaluate and compare a total of 12 available imputation methods from the following four aspects: (i) gene expression recovering, (ii) cell clustering, (iii) gene differential expression, and (iv) cellular trajectory reconstruction. We demonstrate that deep learning-based approaches generally exhibit better overall performance than model-based approaches under major benchmarking comparison, indicating the power of deep learning for imputation. Importantly, we built scIMC (single-cell Imputation Methods Comparison platform), the first online platform that integrates all available state-of-the-art imputation methods for benchmarking comparison and visualization analysis, which is expected to be a convenient and useful tool for researchers of interest. It is now freely accessible via https://server.wei-group.net/scIMC/.
Indexed as
Identifiers
What OpenQuestion holds
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.