本手稿描述了一个资源模块的开发,该模块是名为“NIGMSSandboxforCloud-basedLearning”(https://github.com/NIGMS/NIGMS-Sandbox)的学习平台的一部分。该模块以交互式格式提供有关基于云的共识路径分析的学习材料,该格式使用适当的云资源进行数据访问和分析。路径分析很重要,因为它使我们能够深入了解潜在条件的生物学机制。但是许多途径分析方法的可用性,编码技能的要求,目前的工具只关注少数物种,这使得生物医学研究人员很难有效地进行自我学习和路径分析。此外,缺乏工具,使研究人员能够比较从不同实验和不同分析方法获得的分析结果,以找到一致的结果。为了应对这些挑战,我们设计了一个基于云的,自我学习模块,在已建立的、最先进的Pathway分析技术,为学生和研究人员提供必要的培训和示例材料。训练模块由五个Jupyter笔记本组成,为以下任务提供完整的教程:(i)处理表达式数据,(ii)进行差异分析,可视化和比较从四种差异分析方法获得的结果(limma,t检验,edgeR,DESeq2),(iii)处理三个途径数据库(GO,KEGG和Reactome),(Iv)使用八种方法(ORA,相机,KS测试,Wilcoxon试验,FGSEA,GSA,SAFE和PADOG)和(V)结合了多项分析的结果。我们还提供了一些例子,源代码,为学员提供解释和教学视频,以完成每个JupyterNotebook。该模块支持对许多模型的分析(例如,鼠标,果蝇,斑马鱼)和非模型物种。该模块可在https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-cloud上公开获得。本手稿描述了资源模块的开发,该模块是名为“NIGMSSandboxforCloud-basedLearning\'\'https://github.com/NIGMS/NIGMS-Sandbox”的学习平台的一部分。沙箱的整体起源在本补编开头的社论NIGMS沙箱[1]中进行了描述。该模块以交互式格式提供有关批量和单细胞ATAC-seq数据分析的学习材料,该格式使用适当的云资源进行数据访问和分析。
This manuscript describes the development of a resource module that is part of a learning platform named \'NIGMS Sandbox for Cloud-based Learning\' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based
Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find
consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides
consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/
Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning\'\' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.