Removing batch effects in genomic and epigenomic studies

项目摘要/摘要 组合来自多个研究的高通量生物医学数据集有利于提高统计 在后勤考虑限制样本大小或要求按顺序生成数据的研究中的权力。 然而，通常可以在多批数据中观察到显著的技术异质性，这些数据 由不同的处理或试剂批次、实验者、方案或分析平台产生。这些 所谓的批处理效应混淆了数据中的真实关系，降低了组合多个 批量的数据，甚至可能导致虚假的结果。已经提出了许多方法来过滤技术 来自基因组数据的异质性。这些方法旨在消除批处理效果，未测量或 “替代”变异，或由生物或技术来源引起的其他“有害的”变异。尽管这些 虽然各种方法代表着该领域的重大进展，但仍有重大差距需要解决 从组学数据和其他高通量数据集中适当过滤技术异质性。例如, 许多现有的方法假设相关协变量已知或原始数据通常是独立的。一些人 应用程序需要更细微的校正，包括经常丢失的单细胞转录组数据 细胞类型识别符、微生物组和mrna-seq数据，本质上是组成的，以及成像和空间 具有空间相关数据点的转录数据。此外，批量更正引入了 与调整后的数据的相关性，这需要在下游分析中考虑到，而且大多数 执行批量更正的研究人员没有意识到这种负面影响，并且经常错误地应用 下游分析工具。最后，仍然需要更多的软件工具和基准 用于评估批处理效果方法及其在特定数据集中的有效性的数据集。我们建议开发 算法和软件，以解决研究人员在结合来自 多个实验批次。

Project Summary/Abstract Combining high-throughput biomedical data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects confound true relationships in the data, reducing the power benefits of combining multiple batches of data, and may even lead to spurious results. Many methods have been proposed to filter technical heterogeneity from genomic data. These methods are designed to remove batch effects, unmeasured or “surrogate” variation, or other “unwanted” variation caused by biological or technical sources. Although these approaches represent impactful advances in the field, there are still significant gaps that need to be addressed to appropriately filter technical heterogeneity from -omics data and other high-throughput datasets. For example, many existing methods assume relevant covariates are known or that raw data are generally independent. Some applications require more nuanced correction, including single cell transcriptomics data that are often missing cell-type identifiers, microbiome and mRNA-seq data that are compositional in nature, and imaging and spatial transcriptomics data that have spatially correlated data points. Furthermore, batch correction introduces correlation into the adjusted data, which needs to be accounted for in downstream analyses, and most researchers performing batch correction are unaware of this negative impact and often incorrectly apply downstream analysis tools. Finally, there is still significant need for additional software tools and benchmark datasets for evaluating batch effect methods and their efficacy in specific datasets. We propose to develop algorithms and software to address these specific research gaps facing researchers combining data from multiple experimental batches.