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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/statistical-genomics
课程评论：没有评论
课程名称：基因组数据科学统计学 课程概述：该课程是约翰霍普金斯大学基因组大数据科学专业的第六门课程，旨在介绍与流行的基因组数据科学项目相关的统计学基础。 课程大纲： 第一部分：模块 1 描述：本课程将重点介绍在基因组研究中反复出现的关键概念，如标准化、探索性分析、线性建模、假设检验和多重检验。 第二部分：模块 2 描述：本周我们将讨论预处理、线性建模和批次效应。 第三部分：模块 3 描述：本周我们将学习建模非连续性结果（如二元数据或计数数据）、假设检验和多重假设检验。 第四部分：模块 4 描述：在本周，我们将讨论人们用于分析特定数据类型（如RNA-seq、GWAS、ChIP-Seq和DNA甲基化研究）的常见流程。
Part: 1
Title:Module 1
Description:This course is structured to hit the key conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.
Part: 2
Title:Module 2
Description:This week we will cover preprocessing, linear modeling, and batch effects.
Part: 3
Title:Module 3
Description:This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
Part: 4
Title:Module 4
Description:In this week we will cover a lot of the general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
