Removing batch effects in high-throughput biomedical studies

消除高通量生物医学研究中的批次效应

• 批准号: 10659898
• 负责人: William Evan Johnson
• 金额: $ 30.08万
• 依托单位: RUTGERS BIOMEDICAL AND HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659898
• 关键词: Address; Algorithms; Benchmarking; Biological; Biological Assay; Cells; Cluster Analysis; Computer software; Data; Data Adjustments; Data Correlations; Data Set; Detection; Dimensions; Evaluation; Excision; Generations; Heterogeneity; Image; Lead; Methods; Modeling; Nature; Outcome; Pathway Analysis; Performance; Protocols documentation; Reagent; Research; Research Personnel; Sample Size; Software Tools; Source; Standardization; Structure; Taxonomy; Variant; Work; biomarker development; cell type; combat; data integration; data resource; design; differential expression; epigenomics; experimental study; genomic data; heterogenous data; imaging platform; improved; mRNA sequencing; microbiome; multiple data sources; multiple datasets; novel; outcome prediction; single-cell RNA sequencing; software development; tool; transcriptomics

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 specific and direct correction methods, including single cell transcriptomics data that are often missing cell-type identifiers, microbiome 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.

项目总结/摘要 组合来自多个研究的高通量生物医学数据集有利于增加统计学上的差异。 在逻辑考虑限制样本量或需要连续生成数据的研究中具有把握度。 然而，在多批数据中通常会观察到显著的技术异质性， 从不同的处理或试剂批次、实验者、方案或分析平台产生。这些 所谓的批量效应混淆了数据中的真实关系，降低了组合多个 批量数据，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次效应，不可测量或 “替代”变异或由生物或技术来源引起的其他“不想要的”变异。虽然这些 虽然这些方法代表了该领域的有效进展，但仍存在重大差距，需要加以解决 以适当地过滤来自组学数据和其他高通量数据集的技术异质性。比如说， 许多现有的方法假设相关的协变量是已知的，或者原始数据通常是独立的。一些 应用需要更具体和直接的校正方法，包括单细胞转录组学数据， 通常缺少细胞类型标识符、本质上是组成的微生物组数据以及成像和空间信息。 转录组学数据具有空间相关的数据点。此外，批次校正引入了 调整后的数据的相关性，这需要在下游分析中考虑， 进行批量校正的研究人员没有意识到这种负面影响， 下游分析工具。最后，仍然需要更多的软件工具和基准测试 用于评估批效应方法及其在特定数据集中的有效性的数据集。我们建议发展 算法和软件来解决研究人员面临的这些具体的研究差距， 多个实验批次

项目成果

Robustifying genomic classifiers to batch effects via ensemble learning.

通过集成学习增强基因组分类器的批量效果。

• DOI: 10.1093/bioinformatics/btaa986
• 发表时间: 2021
• 期刊: Bioinformatics (Oxford, England)
• 作者: Zhang,Yuqing;Patil,Prasad;Johnson,WEvan;Parmigiani,Giovanni
• 通讯作者: Parmigiani,Giovanni

Exploring Host-Microbe Interactions in Lung Cancer.

探索肺癌中宿主-微生物的相互作用。

• DOI: 10.1164/rccm.201807-1225ed
• 发表时间: 2018
• 期刊: American journal of respiratory and critical care medicine
• 影响因子: 24.7
• 作者: Zhao,Yue;Johnson,WEvan
• 通讯作者: Johnson,WEvan

animalcules: interactive microbiome analytics and visualization in R.

• DOI: 10.1186/s40168-021-01013-0
• 发表时间: 2021-03-28
• 期刊: Microbiome
• 影响因子: 15.5
• 作者: Zhao Y;Federico A;Faits T;Manimaran S;Segrè D;Monti S;Johnson WE
• 通讯作者: Johnson WE

Interactive analysis of single-cell data using flexible workflows with SCTK2.

• DOI: 10.1016/j.patter.2023.100814
• 发表时间: 2023-08-11
• 期刊: PATTERNS
• 影响因子: 6.5
• 作者: Wang, Yichen;Sarfraz, Irzam;Pervaiz, Nida;Hong, Rui;Koga, Yusuke;Akavoor, Vidya;Cao, Xinyun;Alabdullatif, Salam;Zaib, Syed Ali;Wang, Zhe;Jansen, Frederick;Yajima, Masanao;Johnson, W. Evan;Campbell, Joshua D.
• 通讯作者: Campbell, Joshua D.