ArticleCell regeneration (London, England)2020
Comparison of Scanpy-based algorithms to remove the batch effect from single-cell RNA-seq data.
Article in Cell regeneration (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.PLoS computational biology · 2025Article
- High resolution tissue and cell type identification via single cell transcriptomic profiling.PloS one · 2025Article
- Development of a Spectral Flow Cytometry Analysis Pipeline for High-dimensional Immune Cell Characterization.Journal of immunology (Baltimore, Md. : 1950) · 2024Article
- Development of a Spectral Flow Cytometry Analysis Pipeline for High-Dimensional Immune Cell Characterization.bioRxiv : the preprint server for biology · 2024Article
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- Single-cell biology to decode the immune cellular composition of kidney inflammation.Cell and tissue research · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
With the development of single-cell RNA sequencing (scRNA-seq) technology, analysts need to integrate hundreds of thousands of cells with multiple experimental batches. It is becoming increasingly difficult for users to select the best integration methods to remove batch effects. Here, we compared the advantages and limitations of four commonly used Scanpy-based batch-correction methods using two representative and large-scale scRNA-seq datasets. We quantitatively evaluated batch-correction performance and efficiency. Furthermore, we discussed the performance differences among the evaluated methods at the algorithm level.
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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.