ArticleBMC bioinformatics2021
SSBER: removing batch effect for single-cell RNA sequencing data.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Disturbed flow induces reprogramming of endothelial cells to immune-like and foam cells under hypercholesterolaemia during atherogenesis.Cardiovascular research · 2025Article
- Unified mass imaging maps the lipidome of vertebrate development.Nature methods · 2025Article
- SATINN v2: automated image analysis for mouse testis histology with multi-laboratory data integration†.Biology of reproduction · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences.PloS one · 2023Article
- Single-Cell Genomics: Enabling the Functional Elucidation of Infectious Diseases in Multi-Cell Genomes.Pathogens (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundWith the continuous maturity of sequencing technology, different laboratories or different sequencing platforms have generated a large amount of single-cell transcriptome sequencing data for the same or different tissues. Due to batch effects and high dimensions of scRNA data, downstream analysis often faces challenges. Although a number of algorithms and tools have been proposed for removing batch effects, the current mainstream algorithms have faced the problem of data overcorrection when the cell type composition varies greatly between batches.
resultsIn this paper, we propose a novel method named SSBER by utilizing biological prior knowledge to guide the correction, aiming to solve the problem of poor batch-effect correction when the cell type composition differs greatly between batches.
conclusionsSSBER effectively solves the above problems and outperforms other algorithms when the cell type structure among batches or distribution of cell population varies considerably, or some similar cell types exist across batches.
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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.