Module 4 Overview (1:21) - Module 4 | Coursera

Module 4 Overview (1:21)

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Statistics for Genomic Data Science

Johns Hopkins University

4.2 (332 ratings)

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32K Students Enrolled

Course 6 of 6 in the Genomic Data Science Specialization

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Skills You'll Learn

Statistics, Data Analysis, R Programming, Biostatistics

Reviews

4.2 (332 ratings)

5 stars
53.91%
4 stars
27.10%
3 stars
11.74%
2 stars
2.40%
1 star
4.81%

TK

Mar 24, 2019

Very good course and useful understanding statistical aspects of data.

HD

Apr 8, 2021

This is the best. It opens my eye for genomic data analysis.

From the lesson

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.

Module 4 Overview (1:21)1:21

Gene Set Enrichment (4:19)4:19

More Enrichment (3:59)3:59

Gene Set Analysis in R (7:43)7:43

The Process for RNA-seq (3:59)3:59

The Process for Chip-Seq (5:25)5:25

The Process for DNA Methylation (5:03)5:03

The Process for GWAS/WGS (6:12)6:12

Combining Data Types (eQTL) (6:04)6:04

eQTL in R (10:36)10:36

Researcher Degrees of Freedom (5:49)5:49

Inference vs. Prediction (8:52)8:52

Knowing When to Get Help (2:31)2:31

Statistics for Genomic Data Science Wrap-Up (1:53)1:53

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics

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