利用 JIVE 对单细胞 RNA 测序数据进行批次效应校正。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-09-13 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae134
Joseph Hastings, Donghyung Lee, Michael J O'Connell
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引用次数: 0

摘要

动机在单细胞 RNA 测序分析中,解决批次效应--由不同测序技术、设备和捕获时间等因素产生的技术假象--至关重要。这些因素会导致不必要的变异,并掩盖所关注的潜在生物信号。联合和个体差异解释(JIVE)方法可用于从多源测序数据中提取共同的生物模式,同时调整个体非生物变异(即批次效应)。然而,该方法目前的实现最初是为批量测序数据设计的,因此在计算上不适合大规模单细胞测序数据集:在这项研究中,我们提高了 JIVE 的计算效率,使其更适用于大规模单细胞数据。此外,我们还介绍了 JIVE 在多个单细胞测序数据集上批量效应校正的新应用。我们的增强方法旨在将单细胞测序数据集分解成一个联合结构和一个单独结构,前者捕捉真实的生物变异性,后者捕捉每个批次中的技术变异性。这种联合结构适用于下游分析。我们将其结果与四种流行的工具(Seurat v5、Harmony、LIGER 和 Combat-seq)进行了比较。在保留细胞类型效应方面,以及在批量大小平衡的情况下,JIVE表现最佳:本分析所用的 JIVE 实现可在 https://github.com/oconnell-statistics-lab/scJIVE 上找到。
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Batch-effect correction in single-cell RNA sequencing data using JIVE.

Motivation: In single-cell RNA sequencing analysis, addressing batch effects-technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times-is crucial. These factors can cause unwanted variation and obfuscate the underlying biological signal of interest. The joint and individual variation explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e. batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets.

Results: In this study, we enhance JIVE for large-scale single-cell data by boosting its computational efficiency. Additionally, we introduce a novel application of JIVE for batch-effect correction on multiple single-cell sequencing datasets. Our enhanced method aims to decompose single-cell sequencing datasets into a joint structure capturing the true biological variability and individual structures, which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We benchmarked the results against four popular tools, Seurat v5, Harmony, LIGER, and Combat-seq, which were developed for this purpose. JIVE performed best in terms of preserving cell-type effects and in scenarios in which the batch sizes are balanced.

Availability and implementation: The JIVE implementation used for this analysis can be found at https://github.com/oconnell-statistics-lab/scJIVE.

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