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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/network-biology
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课程名称：系统生物学中的网络分析 课程概述：本课程介绍了当代系统生物学、生物信息学和系统药理学研究中使用的数据整合和统计方法。内容涵盖了处理来自全基因组mRNA表达研究（如微阵列和RNA-seq）的原始数据的方法，包括数据标准化、差异表达、聚类、富集分析和网络构建。课程结合实际操作教程，教授工具使用和管道设置，同时也阐述了所应用方法的数学原理。本课程尤其适合生物学、数学、物理、化学、计算机科学、生物医学和电气工程等领域的研究生和高级本科生。对于遇到大数据集的研究人员，本课程也将提供实用价值。课程展示了梅奥实验室开发的软件工具和其他免费可用的数据分析与可视化工具，旨在帮助参与者将课程中介绍的方法应用于自己的数据分析项目。 课程大纲： 1. **课程概述与介绍**：讨论复杂系统的概念，将细胞视为复杂系统的一个案例，介绍非生物背景的工程师对细胞及分子生物学的入门知识。 2. **拓扑与网络演化模型**：提供网络分析在系统生物学中的历史视角，重点介绍基于简单规则的网络演化模型。 3. **生物网络类型**：讲解系统生物学和系统药理学中构建和分析的各种生物网络，特别是功能关联网络（FAN）。 4. **数据处理与差异表达基因识别**：讨论数据标准化方法，及如何利用梅奥实验室开发的特征方向法识别差异表达基因。 5. **基因集富集与网络分析**：重点介绍梅奥实验室开发的分析基因集的工具，如Enrichr和GSEA，讨论基因集富集分析的改进方法。 6. **深度测序数据处理与分析**：讲解RNA-seq和ChIP-seq数据的基础步骤与热门分析管道，包括UNIX/Linux命令和R编程元素。 7. **主成分分析、自组织映射及聚类方法**：分析不同聚类方法的理论及其在R和MATLAB中的实际应用。 8. **数据整合资源**：讨论构建和分析系统生物学和药理学中的功能关联网络，如何利用这些网络分析基因列表。 9. **众包：微任务与巨任务**：介绍众包方法，提供参与微任务和巨任务项目的机会，以合作解决难题。 10. **期末考试**：通过多项选择题考察课程所覆盖的所有模块内容，一些问题可能需要在新数据集上应用所学分析方法。 该课程将为希望在计算系统生物学领域获得实际技能的学生和研究人员提供全面的知识和实践机会。
Name:Course Overview and Introductions
Description:The 'Introduction to Complex Systems' module discusses complex systems and leads to the idea that a cell can be considered a complex system or a complex agent living in a complex environment just like us. The 'Introduction to Biology for Engineers' module provides an introduction to some central topics in cell and molecular biology for those who do not have the background in the field. This is not a comprehensive coverage of cell and molecular biology. The goal is to provide an entry point to motivate those who are interested in this field, coming from other disciplines, to begin studying biology.
Name:Topological and Network Evolution Models
Description:In the 'Topological and Network Evolution Models' module, we provide several lectures about a historical perspective of network analysis in systems biology. The focus is on in-silico network evolution models. These are simple computational models that, based of few rules, can create networks that have a similar topology to the molecular networks observed in biological systems.
Name:Types of Biological Networks
Description:The 'Types of Biological Networks' module is about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. This lecture ends with the idea of functional association networks (FANs). Following this lecture are lectures that discuss how to construct FANs and how to use these networks for analyzing gene lists.
Name:Data Processing and Identifying Differentially Expressed Genes
Description:This set of lectures in the 'Data Processing and Identifying Differentially Expressed Genes' module first discusses data normalization methods, and then several lectures are devoted to explaining the problem of identifying differentially expressed genes with the focus on understanding the inner workings of a new method developed by the Ma'ayan Laboratory called the Characteristic Direction.
Name:Gene Set Enrichment and Network Analyses
Description:In the 'Gene Set Enrichment and Network Analyses' module the emphasis is on tools developed by the Ma'ayan Laboratory to analyze gene sets. Several tools will be discussed including: Enrichr, GEO2Enrichr, Expression2Kinases and DrugPairSeeker. In addition, one lecture will be devoted to a method we call enrichment vector clustering we developed, and two lectures will describe the popular gene set enrichment analysis (GSEA) method and an improved method we developed called principal angle enrichment analysis (PAEA).
Name:Deep Sequencing Data Processing and Analysis
Description:A set of lectures in the 'Deep Sequencing Data Processing and Analysis' module will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures. These lectures also cover UNIX/Linux commands and some programming elements of R, a popular freely available statistical software. Note that since these lectures were developed and recorded during the Fall of 2013, it is possible that there are better tools that should be used now since the field is rapidly advancing.
Name:Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering
Description:This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. The theory behind these methods of analysis are covered in detail, and this is followed by some practical demonstration of the methods for applications using R and MATLAB.
Name:Resources for Data Integration
Description:The lectures in the 'Resources for Data Integration' module are about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. These lectures start with the idea of functional association networks (FANs). Following this lecture are several lectures that discuss how to construct FANs from various resources and how to use these networks for analyzing gene lists as well as to construct a puzzle that can be used to connect genomic data with phenotypic data.
Name:Crowdsourcing: Microtasks and Megatasks
Description:The final set of lectures presents the idea of crowdsourcing. MOOCs provide the opportunity to work together on projects that are difficult to complete alone (microtasks) or compete for implementing the best algorithms to solve hard problems (megatasks). You will have the opportunity to participate in various crowdsourcing projects: microtasks and megatasks. These projects are designed specifically for this course.
Name:Final Exam
Description:The final exam consists of multiple choice questions from topics covered in all of modules of the course. Some of the questions may require you to perform some of the analysis methods you learned throughout the course on new datasets.
An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.
