scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.
scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.
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scDisInFact：用于整合和预测多批次多条件单细胞 RNA 测序数据的解缠结学习。
DOI：
10.1101/2023.05.01.538975
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zhang,Xiuwei
中科院分区：
文献类型：
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作者：
Zhang,Ziqi;Zhao,Xinye;Qiu,Peng;Zhang,Xiuwei
单细胞RNA测序（scRNA-seq）已被广泛用于疾病研究，其中样品批次是从不同条件下的供体收集的，包括人口统计学组、疾病阶段和药物治疗。值得注意的是，此类研究中样品批次之间的差异是批次效应引起的技术混杂因素和条件效应引起的生物学变异的混合。然而，现有的批量效应去除方法往往同时去除技术批量效应和有意义的条件效应，而微扰预测方法只关注条件效应，由于批量效应未被考虑，导致基因表达预测不准确。在这里，我们介绍了scDisInFact，这是一个深度学习框架，可以对scRNA-seq数据中的批量效应和条件效应进行建模。scDisInFact学习将条件效应从批次效应中分离出来的潜在因素，使其能够同时执行三项任务：批次效应消除，条件相关关键基因检测和扰动预测。我们在模拟和真实的数据集上评估了scDisInFact，并将其性能与每个任务的基线方法进行了比较。我们的研究结果表明，scDisInFact优于专注于单个任务的现有方法，为整合和预测多批次多条件单细胞RNA测序数据提供了更全面和准确的方法。
Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluate scDisInFact on both simulated and real datasets, and compare its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.