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所在平台: Udemy |
课程主页: https://www.udemy.com/course/differential-gene-expression-analysis/
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课程名称：差异基因表达分析 - 完整的A到Z指南 课程概述： 您是否想成为一名生物信息学家，但不知道这包含哪些内容？或者您在生物数据分析中遇到困难？是否对生物、医学、统计和分析术语感到困惑？您是否希望在这一领域成为专家，能够设计生物实验，恰当地应用相关概念，并进行完整的端到端分析？本课程将全面讲解差异基因表达分析，重点关注下一代测序、RNA测序（RNAseq）和定量PCR（qPCR）。 课程内容： 在本课程中，您将学习生物信息学中最受欢迎的子领域之一：差异基因表达分析。课程结束时，您将能够独立进行基于RNAseq和qPCR的差异基因表达分析，并使用R编程语言进行分析。 RNAseq部分是课程中最全面的，包括从FASTQ文库到完成差异表达分析所需的所有技能。虽然课程侧重于R作为生物分析环境的选择，但您也有机会学习UNIX终端基础的TUXEDO管道及在线工具。此外，您将扎实掌握统计和建模方法，以便有效地解释并使用这些方法解决生物信息学中的差异基因表达分析问题。 在本课程中，您将学习到以下内容并能够做到： - 使用R和TUXEDO管道完成端到端的RNAseq分析：从FASTQ文库开始，进行比对、转录组组装、基因组注释、读数计数和差异评估。 - 在R中进行qPCR分析：学习delta-Ct方法、delta-delta-Ct方法、实验设计和数据解释。 - 将分子生物学知识应用于解决差异基因表达分析问题，特别是在生物信息学领域。 - 理解qPCR、微阵列、测序和RNAseq的技术基础，以自信应对差异基因表达数据，理解数字的含义。 - 在R中使用差异基因表达的两种主要建模方法：一般线性模型和非参数秩乘法框架。 - 学习通路分析方法及其在假设生成中的应用。 - 可视化实验中的基因表达数据。 通过本课程，您将获得实践分析和实验设计经验，处理突发问题时也会有理论支持。
Do you want to be a bioinformatician but don't know what it entails? Or perhaps you're struggling with biological data analysis problems? Are you confused amongst the biological, medicals, statistical and analytical terms? Do you want to be an expert in this field and be able to design biological experiments, appropriately apply the concepts and do a complete end-to-end analysis?This is a comprehensive and all-in-one-place course that will teach you differential gene expression analysis with focus on next-generation sequencing, RNAseq and quantitative PCR (qPCR)In this course we'll learn together one of the most popular sub-specialities in bioinformatics: differential gene expression analysis. By the end of this course you'll be able to undertake both RNAseq and qPCR based differential gene expression analysis, independently and by yourself, in R programming language. The RNAseq section of the course is the most comprehensive and includes everything you need to have the skills required to take FASTQ library of next-generation sequencing reads and end up with complete differential expression analysis. Although the course focuses on R as a biological analysis environment of choice, you'll also have the opportunity not only to learn about UNIX terminal based TUXEDO pipeline, but also online tools. Moreover you'll become well grounded in the statistical and modelling methods so you can explain and use them effectively to address bioinformatic differential gene expression analysis problems. The course has been made such that you can get a blend of hands-on analysis and experimental design experience - the practical side will allow you to do your analysis, while theoretical side will help you face unexpected problems. Here is the summary of what will be taught and what you'll be able to do by taking this course:You'll learn and be able to do a complete end-to-end RNAseq analysis in R and TUXEDO pipelines: starting with FASTQ library through doing alignment, transcriptome assembly, genome annotation, read counting and differential assessmentYou'll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretationYou'll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generallyYou'll learn the technical foundations of qPCR, microarray, sequencing and RNAseq so that you can confidently deal with differential gene expression data by understanding what the numbers meanYou'll learn and be able to use two main modelling methods in R used for differential gene expression: the general linear model as well as non-parametric rank product frameworksYou'll learn about pathway analysis methods and how they can be used for hypothesis generationYou'll learn and be able to visualise gene expression data from your experiments
