關於此課程：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://icahn.mssm.edu/research/labs/maayan-laboratory) 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.
製作方： 西奈山伊坎医学院
教學方： Avi Ma’ayan, PhD, Director, Mount Sinai Center for Bioinformatics
Professor, Department of Pharmacological Sciences
| 基本信息 | 課程 3（共 6 門，系统生物学与生物技术 Specialization ） |
| 承諾學習時間 | 6-8小时/周 |
| 語言 | English |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
授課大綱
第 1 週
Course Overview and Introductions
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.
3 個視頻, 4 個閱讀材料
- Reading: Course Logistics
- Reading: Grading Policy
- Reading: Resources and Links to Additional Materials
- Reading: MATLAB License
- 視頻: Introduction to Cell Biology
- 視頻: Introduction to Molecular Biology
已評分: Introduction to Complex Systems
已評分: Introduction to Cell Biology
已評分: Introduction to Molecular Biology
第 2 週
Topological and Network Evolution Models
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.
4 個視頻
- 視頻: Duplication-Divergence and Network Motifs
- 視頻: Large Size Motifs and Complex Models of Network Evolution
- 視頻: Network Properties of Biological Networks
已評分: Rich-Get-Richer
已評分: Duplication-Divergence and Network Motifs
已評分: Large Size Motifs
已評分: Topological Properties of Biological Networks
第 3 週
Types of Biological Networks
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.
4 個視頻
- 視頻: Genes2Networks and Network Visualization
- 視頻: Sets2Networks - Creating Functional Association Networks
- 視頻: Genes2FANs - Analyzing Gene Lists with Functional Association Networks
已評分: Types of Biological Networks
已評分: Genes2Networks and Network Visualization
已評分: Functional Association Networks with Sets2Networks
已評分: Functional Association Networks with Genes2FANs
第 4 週
Data Processing and Identifying Differentially Expressed Genes
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.
5 個視頻
- 視頻: Characteristic Direction Method - Part 1
- 視頻: Characteristic Direction Method - Part 2
- 視頻: Characteristic Direction Method - Part 3
- 視頻: Characteristic Direction Method - Part 4
已評分: Data Normalization
已評分: Characteristic Direction
第 5 週
Gene Set Enrichment and Network Analyses
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).
9 個視頻, 1 個閱讀材料
- 視頻: GEO2Enrichr: A Google Chrome Extension for Gene Set Extraction and Enrichment
- 視頻: Gene Set Enrichment Analysis (GSEA) - Preliminaries
- 視頻: Gene Set Enrichment Analysis (GSEA) - Part 2
- 視頻: Principal Angle Enrichment Analysis (PAEA)
- 視頻: Network2Canvas (N2C) and Enrichment Analysis with N2C
- Reading: GATE Desktop Software Tool
- 視頻: Expression2Kinases: Inferring Pathways from Differentially Expressed Genes
- 視頻: DrugPairSeeker and the New CMAP
- 視頻: Classifying Patients/Tumors from TCGA
已評分: The Fisher Exact Test and Enrichr
已評分: Gene Set Enrichment Analysis (GSEA) - Part 1
已評分: Gene Set Enrichment Analysis (GSEA) - Part 2
已評分: Principal Angle Enrichment Analysis (PAEA)
已評分: GATE and Network2Canvas
已評分: Expression2Kinases
已評分: DrugPairSeeker and the New CMAP
已評分: Classifying Patients from TCGA
第 6 週
Deep Sequencing Data Processing and Analysis
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.
7 個視頻
- 視頻: RNA-seq Analysis - Using TopHat and Cufflinks
- 視頻: RNA-seq Analysis - R Basics
- 視頻: RNA-seq Analysis - CummeRbund
- 視頻: STAR: An Ultra-fast RNA-seq Aligner
- 視頻: ChIP-seq Analysis - Part 1
- 視頻: ChIP-seq Analysis - Part 2
已評分: RNA-seq and UNIX/Linux Commands
已評分: RNA-seq Pipeline
已評分: CummeRbund and R Programming
已評分: CummeRbund - Demo
已評分: RNA-seq STAR
已評分: ChIP-seq Analysis - Part 1
已評分: ChIP-seq Analysis - Part 2
第 7 週
Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering
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.
6 個視頻, 1 個閱讀材料
- 視頻: Principal Component Analysis (PCA) - Part 2
- 視頻: Principal Component Analyis (PCA) Plotting in MATLAB
- Reading: MATLAB License
- 視頻: Clustergram in MATLAB
- 視頻: Self-Organizing Maps
- 視頻: Network-Based Clustering
已評分: Principal Component Analysis (PCA) - Part 1
已評分: Principal Component Analysis (PCA) - Part 2
已評分: Principal Component Analysis (PCA) with MATLAB
已評分: Hierarchical Clustering (HC) with MATLAB
已評分: Self-Organizing Maps
已評分: Network-Based Clustering
第 8 週
Resources for Data Integration
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.
5 個視頻
- 視頻: Resources for Data Integration - Part 1
- 視頻: Resources for Data Integration - Part 2
- 視頻: Resources for Data Integration - Part 3
- 視頻: Resources for Data Integration - Part 4
已評分: Big Data in Biology and Data Integration
已評分: Resources for Data Integration
第 9 週
Crowdsourcing: Microtasks and Megatasks
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.
2 個視頻
- 視頻: Crowdsourcing Tasks for this Course
已評分: Crowdsourcing: Microtasks and Megatasks
第 10 週
Final Exam
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.
已評分: Final Exam
常見問題解答
運作方式
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製作方
西奈山伊坎医学院
The Icahn School of Medicine at Mount Sinai, in New York City is a leader in medical and scientific training and education, biomedical research and patient care.
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
helpful
Very good course
Excellent course .
PC
The course material was great, and covered many interesting topics. But I found many of the problems didn't really test your understanding of the material, just whether you remembered some fact from the lectures.