ArticleBriefings in bioinformatics2022
Comprehensive evaluation of noise reduction methods for single-cell RNA sequencing data.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Assessment of dispersion metrics for estimating single-cell transcriptional variability.PLoS computational biology · 2026Article
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease.Circulation research · 2026Review
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets.Nature communications · 2025Article
- Review
- Combining single-cell ATAC and RNA sequencing for supervised cell annotation.BMC bioinformatics · 2025Article
- Enhancing single-cell transcriptomics using interposed anchor oligonucleotide sequences.Communications biology · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Single-cell transcriptomics for immune profiling of cerebrospinal fluid in neurological diseases.Frontiers in immunology · 2025Review
- Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging.Cell reports methods · 2024Article
- scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.Briefings in bioinformatics · 2024Article
- Calibrating dimension reduction hyperparameters in the presence of noise.PLoS computational biology · 2024Article
- Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.bioRxiv : the preprint server for biology · 2024Article
- Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting.Development (Cambridge, England) · 2024Article
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- A review of single-cell transcriptomics and epigenomics studies in maternal and child health.Epigenomics · 2024Review
- Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives.Plant communications · 2023Review
- Expression quantitative trait locus studies in the era of single-cell omics.Frontiers in genetics · 2023Review
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Authors and funding
4 authors.
Funding
Abstract
Normalization and batch correction are critical steps in processing single-cell RNA sequencing (scRNA-seq) data, which remove technical effects and systematic biases to unmask biological signals of interest. Although a number of computational methods have been developed, there is no guidance for choosing appropriate procedures in different scenarios. In this study, we assessed the performance of 28 scRNA-seq noise reduction procedures in 55 scenarios using simulated and real datasets. The scenarios accounted for multiple biological and technical factors that greatly affect the denoising performance, including relative magnitude of batch effects, the extent of cell population imbalance, the complexity of cell group structures, the proportion and the similarity of nonoverlapping cell populations, dropout rates and variable library sizes. We used multiple quantitative metrics and visualization of low-dimensional cell embeddings to evaluate the performance on batch mixing while preserving the original cell group and gene structures. Based on our results, we specified technical or biological factors affecting the performance of each method and recommended proper methods in different scenarios. In addition, we highlighted one challenging scenario where most methods failed and resulted in overcorrection. Our studies not only provided a comprehensive guideline for selecting suitable noise reduction procedures but also pointed out unsolved issues in the field, especially the urgent need of developing metrics for assessing batch correction on imperceptible cell-type mixing.
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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.