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.
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课程信息
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您将获得的技能
StatisticsData AnalysisR ProgrammingBiostatistics
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Module 1
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.
21 个视频 （总计 129 分钟）, 3 个阅读材料, 1 个测验
21 个视频
What is Statistics?2分钟
Finding Statistics You Can Trust (4:44)4分钟
Getting Help (3:44)3分钟
What is Data? (4:28)4分钟
Representing Data (5:23)5分钟
Module 1 Overview (1:07)1分钟
Reproducible Research (3:42)3分钟
Achieving Reproducible Research (5:02)5分钟
R Markdown (6:26)6分钟
The Three Tables in Genomics (2:10)2分钟
The Three Tables in Genomics (in R) (3:46)3分钟
Experimental Design: Variability, Replication, and Power (14:17)14分钟
Experimental Design: Confounding and Randomization (9:26)9分钟
Exploratory Analysis (9:21)9分钟
Exploratory Analysis in R Part I (7:22)7分钟
Exploratory Analysis in R Part II (10:07)10分钟
Exploratory Analysis in R Part III (7:26)7分钟
Data Transforms (7:31)7分钟
Clustering (8:43)8分钟
Clustering in R (9:09)9分钟
3 个阅读材料
Syllabus10分钟
Pre Course Survey10分钟
Introduction and Materials10分钟
1 个练习
Module 1 Quiz20分钟
周
2完成时间为 2 小时
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 个视频 （总计 97 分钟）, 1 个测验
14 个视频
Dimension Reduction (12:13)12分钟
Dimension Reduction (in R) (8:48)8分钟
Pre-processing and Normalization (11:26)11分钟
Quantile Normalization (in R) (4:49)4分钟
The Linear Model (6:50)6分钟
Linear Models with Categorical Covariates (4:08)4分钟
Adjusting for Covariates (4:16)4分钟
Linear Regression in R (13:03)13分钟
Many Regressions at Once (3:50)3分钟
Many Regressions in R (7:21)7分钟
Batch Effects and Confounders (7:11)7分钟
Batch Effects in R: Part A (8:18)8分钟
Batch Effects in R: Part B (3:50)3分钟
1 个练习
Module 2 Quiz20分钟
周
3完成时间为 2 小时
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 个视频 （总计 86 分钟）, 1 个测验
15 个视频
Logistic Regression (7:03)7分钟
Regression for Counts (5:02)5分钟
GLMs in R (9:28)9分钟
Inference (4:18)4分钟
Null and Alternative Hypotheses (4:45)4分钟
Calculating Statistics (5:11)5分钟
Comparing Models (7:08)7分钟
Calculating Statistics in R9分钟
Permutation (3:26)3分钟
Permutation in R (3:33)3分钟
P-values (6:04)6分钟
Multiple Testing (8:25)8分钟
P-values and Multiple Testing in R: Part A (5:58)5分钟
P-values and Multiple Testing in R: Part B (4:23)4分钟
1 个练习
Module 3 Quiz20分钟
周
4完成时间为 2 小时
Module 4
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.
14 个视频 （总计 74 分钟）, 1 个阅读材料, 1 个测验
14 个视频
Gene Set Enrichment (4:19)4分钟
More Enrichment (3:59)3分钟
Gene Set Analysis in R (7:43)7分钟
The Process for RNA-seq (3:59)3分钟
The Process for Chip-Seq (5:25)5分钟
The Process for DNA Methylation (5:03)5分钟
The Process for GWAS/WGS (6:12)6分钟
Combining Data Types (eQTL) (6:04)6分钟
eQTL in R (10:36)10分钟
Researcher Degrees of Freedom (5:49)5分钟
Inference vs. Prediction (8:52)8分钟
Knowing When to Get Help (2:31)2分钟
Statistics for Genomic Data Science Wrap-Up (1:53)1分钟
1 个阅读材料
Post Course Survey10分钟
1 个练习
Module 4 Quiz10分钟
4.1
37 条评论来自基因组数据科学所需的统计学的热门评论
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
I have really enjoyed the course and I have learnt different concepts relevant for my current study.\n\nYurany
讲师
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
关于 约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
关于 基因组数据科学 专项课程
With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.
This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.
This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.
To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
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