数据驱动的批量检测增强了单细胞组学数据分析。

Ziqi Zhang, Xiuwei Zhang
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引用次数: 0

摘要

在单细胞组学研究中,数据通常是跨多个批次收集的,这就产生了批次效应:技术混杂因素会带来噪声并扭曲数据分布。校正这些效应具有挑战性,因为它们来源不明、非线性失真,而且很难准确地将数据分配到最适合整合方法的批次中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven batch detection enhances single-cell omics data analysis.

In single-cell omics studies, data are typically collected across multiple batches, resulting in batch effects: technical confounders that introduce noise and distort data distribution. Correcting these effects is challenging due to their unknown sources, nonlinear distortions, and the difficulty of accurately assigning data to batches that are optimal for integration methods.

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