An order-preserving batch-effect correction method based on a monotonic deep learning framework

doi:10.1093/bib/bbaf247

2025-05-01

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Mingxuan Zhang

Yinglei Lai *

DOI：10.1093/bib/bbaf247

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摘要

En 中文

单细胞RNA测序显著加深了我们对细胞异质性和基因调控的理解。批次效应校正对于实现稳健的数据集成至关重要。已开发多种方法以解决此问题，特别是涉及锚定或深度学习等组件的流程方法，已取得显著成功。然而，作为一项重要特征，顺序保持性在流程方法中大多被忽视。基于单调深度学习网络，我们开发了一种具有顺序保持特性的校正方法。通过与现有方法比较，我们证明了该方法有效提升了聚类性能，更好地保留了原始基因间相关性及差异表达信息。

Keyword:

scRNA-sequencing

batch effect

order-preserving

monotonic deep learning network

inter-gene correlation

differential expression consistency

期刊

Briefings in Bioinformatics

IF:

7.7

论文数:

5.6K

被引数:

2.7W

机构

Chinese Academy of Sciences

学者数:

3.9W

论文数: 1.5W

被引数: 58.4W

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