Vasaikar Suhas V, Savage Adam K, Gong Qiuyu, Swanson Elliott, Talla Aarthi, Lord Cara, Heubeck Alexander T, Reading Julian, Graybuck Lucas T, Meijer Paul, Torgerson Troy R, Skene Peter J, Bumol Thomas F, Li Xiao-Jun
Allen Institute for Immunology, Seattle, WA, 98109, USA.
Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
Nat Commun. 2023 Mar 27;14(1):1684. doi: 10.1038/s41467-023-37432-w.
Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO ( https://github.com/aifimmunology/PALMO ), a platform that contains five analytical modules to examine longitudinal bulk and single-cell multi-omics data from multiple perspectives, including decomposition of sources of variations within the data, collection of stable or variable features across timepoints and participants, identification of up- or down-regulated markers across timepoints of individual participants, and investigation on samples of same participants for possible outlier events. We have tested PALMO performance on a complex longitudinal multi-omics dataset of five data modalities on the same samples and six external datasets of diverse background. Both PALMO and our longitudinal multi-omics dataset can be valuable resources to the scientific community.
用于生物学和临床研究的纵向组学数据和单细胞组学数据越来越多,但由于其存在多种内在变异类型,分析起来具有挑战性。我们展示了PALMO(https://github.com/aifimmunology/PALMO),这是一个包含五个分析模块的平台,可从多个角度检查纵向组学数据和单细胞多组学数据,包括数据内变异来源的分解、跨时间点和参与者收集稳定或可变特征、识别个体参与者各时间点上调或下调的标志物,以及调查同一参与者的样本是否存在异常事件。我们在同一样本上的包含五种数据模式的复杂纵向多组学数据集以及六个不同背景的外部数据集上测试了PALMO的性能。PALMO和我们的纵向多组学数据集对科学界而言都可能是有价值的资源。