SCITUNA: single-cell data integration tool using network alignment

Aissa Houdjedj

doi:10.1186/s12859-025-06087-3

2025-03-27

Aissa Houdjedj

Yacine Marouf

Mekan Myradov

Dogan, Sueleyman Onur

DOI：10.1186/s12859-025-06087-3

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摘要

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背景随着单细胞基因组学实验复杂性和规模的增加，整合多个数据集的需求日益增长。这种整合通过利用更大的数据量来增强细胞特征识别。然而，批次效应——由实验室、时间或协议差异引起的实验变异性——构成了重大挑战。尽管已提出多种批次校正方法，但许多方法仍存在局限性，例如仅输出降维数据、依赖计算密集型模型，或导致对细胞类型组成多样的批次产生过度校正。结果我们介绍一种名为SCITUNA的新型批次效应校正方法，即“基于网络对齐的单细胞数据整合工具”。我们在来自四个真实数据集和一个人工模拟数据集的39个独立批次上进行了评估，这些数据集包含scRNA-seq和scATAC-seq数据，涵盖多种物种和组织。通过13项指标对现有批次校正方法进行详细比较后发现，SCITUNA优于当前方法，并能成功保留原始数据中的生物信号。具体而言，除肺数据集的多批次整合（差异为0.004）外，SCITUNA在所有比较中均表现优于当前方法。结论 SCITUNA能够有效去除批次效应，同时保留数据中的生物信号。我们的广泛实验表明，SCITUNA将成为各类整合任务的有价值的工具。

Keyword:

Single-cell data integration

Batch effect

Rare cell types

Iterative correction

期刊

BMC Bioinformatics

IF:

3.3

论文数:

597

被引数:

5.2W

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Univ Arizona

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