scBatch：通过样本距离矩阵调整对 RNA-seq 数据进行批次效应校正。
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.
机构信息
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
出版信息
Bioinformatics. 2020 May 1;36(10):3115-3123. doi: 10.1093/bioinformatics/btaa097.
Abstract
MOTIVATION
Batch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data.
RESULTS
We present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods.
AVAILABILITY AND IMPLEMENTATION
The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
动机
批次效应是深度测序数据分析中经常面临的挑战，它可能导致误导性的结论。现有的方法不能很好地纠正批次效应，特别是在单细胞 RNA 测序（RNA-seq）数据中。
结果
我们提出了 scBatch，这是一种用于批量和单细胞 RNA-seq 数据的批次效应校正的数值算法，重点是改进聚类和基因差异表达分析。scBatch 不受批次效应产生机制的假设的限制。正如模拟和真实数据分析所示，scBatch 优于基准批次效应校正方法。
可用性和实现
R 包可在 github.com/tengfei-emory/scBatch 获得。本文中生成结果和图的代码可在 github.com/tengfei-emory/scBatch-paper-scripts 获得。
补充信息
补充数据可在《生物信息学》在线获得。
相似文献
1
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.scBatch：通过样本距离矩阵调整对 RNA-seq 数据进行批次效应校正。
Bioinformatics. 2020 May 1;36(10):3115-3123. doi: 10.1093/bioinformatics/btaa097.
2
Mitigating the adverse impact of batch effects in sample pattern detection.减轻样本模式检测中批次效应的不利影响。
Bioinformatics. 2018 Aug 1;34(15):2634-2641. doi: 10.1093/bioinformatics/bty117.
3
ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.ResPAN：通过残差对抗网络对 scRNA-seq 数据进行强大的批量校正模型。
Bioinformatics. 2022 Aug 10;38(16):3942-3949. doi: 10.1093/bioinformatics/btac427.
4
2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions.2DImpute：基于二维相关性的单细胞 RNA-seq 数据插补。
Bioinformatics. 2020 Jun 1;36(11):3588-3589. doi: 10.1093/bioinformatics/btaa148.
5
Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.基于结构正则化领域自适应的单细胞 RNA-seq 数据半监督聚类和注释。
Bioinformatics. 2021 May 5;37(6):775-784. doi: 10.1093/bioinformatics/btaa908.
6
HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.HDMC：一种用于去除单细胞 RNA-seq 数据中批次效应的新型深度学习框架。
Bioinformatics. 2022 Feb 7;38(5):1295-1303. doi: 10.1093/bioinformatics/btab821.
7
Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning.单细胞 RNA-seq 解释使用进化多目标集成修剪。
Bioinformatics. 2019 Aug 15;35(16):2809-2817. doi: 10.1093/bioinformatics/bty1056.
8
scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.scBGEDA：基于双分图集成分聚类的对偶去噪自动编码器的单细胞聚类分析。
Bioinformatics. 2023 Feb 14;39(2). doi: 10.1093/bioinformatics/btad075.
9
bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.bayNorm：用于单细胞 RNA-seq 数据的贝叶斯基因表达恢复、插补和标准化。
Bioinformatics. 2020 Feb 15;36(4):1174-1181. doi: 10.1093/bioinformatics/btz726.
10
SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.SPsimSeq：批量和单细胞 RNA-seq 数据的半参数模拟。
Bioinformatics. 2020 May 1;36(10):3276-3278. doi: 10.1093/bioinformatics/btaa105.
引用本文的文献
1
Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.探索单细胞RNA测序：方案、工具、数据库及应用
Genomics Inform. 2025 May 17;23(1):13. doi: 10.1186/s44342-025-00044-5.
2
The effect of data transformation on low-dimensional integration of single-cell RNA-seq.数据转换对单细胞 RNA-seq 低维整合的影响。
BMC Bioinformatics. 2024 Apr 30;25(1):171. doi: 10.1186/s12859-024-05788-5.
3
Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing.使用靶向RNA测序技术分析结直肠癌中免疫和炎症基因的差异基因表达
Front Oncol. 2023 Oct 5;13:1206482. doi: 10.3389/fonc.2023.1206482. eCollection 2023.
4
Single-cell profiling in multiple myeloma: insights, problems, and promises.多发性骨髓瘤的单细胞分析：见解、问题和前景。
Blood. 2023 Jul 27;142(4):313-324. doi: 10.1182/blood.2022017145.
5
Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome.拟南芥植物转录组的太空飞行和微重力反应的荟萃分析。
NPJ Microgravity. 2023 Mar 20;9(1):21. doi: 10.1038/s41526-023-00247-6.
6
SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis.SelectBCM工具：一种用于批量转录组分析的批量评估框架，用于选择最合适的批量校正方法。
NAR Genom Bioinform. 2023 Mar 3;5(1):lqad014. doi: 10.1093/nargab/lqad014. eCollection 2023 Mar.
7
The Sum of Two Halves May Be Different from the Whole-Effects of Splitting Sequencing Samples Across Lanes.两半之和可能与跨道分割测序样本的整体效果不同。
Genes (Basel). 2022 Dec 1;13(12):2265. doi: 10.3390/genes13122265.
8
WNT5A drives interleukin-6-dependent epithelial-mesenchymal transition via the JAK/STAT pathway in keloid pathogenesis.在瘢痕疙瘩发病机制中，WNT5A通过JAK/STAT途径驱动白细胞介素-6依赖性上皮-间质转化。
Burns Trauma. 2022 Oct 7;10:tkac023. doi: 10.1093/burnst/tkac023. eCollection 2022.
9
A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.基于数据增强和弹性数据共享套索正则化的基因表达新型元分析。
BMC Bioinformatics. 2022 Aug 23;23(Suppl 10):353. doi: 10.1186/s12859-022-04887-5.
10
Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).基于经验贝叶斯方法（BRIDGE）减少具有相关性样本的微阵列数据的批次效应。
Stat Appl Genet Mol Biol. 2021 Dec 14;20(4-6):101-119. doi: 10.1515/sagmb-2021-0020.
本文引用的文献
1
: batch effect adjustment for RNA-seq count data.RNA测序计数数据的批次效应调整
NAR Genom Bioinform. 2020 Sep;2(3):lqaa078. doi: 10.1093/nargab/lqaa078. Epub 2020 Sep 21.
2
Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset.基于先验基因-基因关联的批量校正评估框架：在 GTEx 数据集上的应用。
BMC Bioinformatics. 2019 May 28;20(1):268. doi: 10.1186/s12859-019-2855-9.
3
A test metric for assessing single-cell RNA-seq batch correction.一种用于评估单细胞 RNA-seq 批次校正的测试指标。
Nat Methods. 2019 Jan;16(1):43-49. doi: 10.1038/s41592-018-0254-1. Epub 2018 Dec 20.
4
Dimensionality reduction for visualizing single-cell data using UMAP.使用UMAP进行单细胞数据可视化的降维方法。
Nat Biotechnol. 2018 Dec 3. doi: 10.1038/nbt.4314.
5
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.通过匹配相互最近邻，纠正单细胞 RNA 测序数据中的批次效应。
Nat Biotechnol. 2018 Jun;36(5):421-427. doi: 10.1038/nbt.4091. Epub 2018 Apr 2.
6
Mitigating the adverse impact of batch effects in sample pattern detection.减轻样本模式检测中批次效应的不利影响。
Bioinformatics. 2018 Aug 1;34(15):2634-2641. doi: 10.1093/bioinformatics/bty117.
7
Glycogen synthase kinase 3β inhibition enhances Notch1 recycling.糖原合酶激酶 3β 抑制增强 Notch1 的循环。
Mol Biol Cell. 2018 Feb 15;29(4):389-395. doi: 10.1091/mbc.E17-07-0474. Epub 2017 Dec 13.
8
Missing data and technical variability in single-cell RNA-sequencing experiments.单细胞 RNA 测序实验中的数据缺失和技术变异性。
Biostatistics. 2018 Oct 1;19(4):562-578. doi: 10.1093/biostatistics/kxx053.
9
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes.使用对照和靶标基因控制单细胞 RNA 测序研究中的混杂效应。
Sci Rep. 2017 Oct 19;7(1):13587. doi: 10.1038/s41598-017-13665-w.
10
Removal of batch effects using distribution-matching residual networks.使用分布匹配残差网络消除批次效应。
Bioinformatics. 2017 Aug 15;33(16):2539-2546. doi: 10.1093/bioinformatics/btx196.
Zotero 插件