Statistics for Genomic Data Science

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Statistics for Genomic Data Science

This course is part of Genomic Data Science Specialization

Instructor: Jeff Leek, PhD

40,678 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

380 reviews

9 hours to complete

Flexible schedule

Learn at your own pace

91%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

380 reviews

9 hours to complete

Flexible schedule

Learn at your own pace

91%

Most learners liked this course

Skills you'll gain

Tools you'll learn

R Programming

Details to know

Shareable certificate

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Assessments

4 assignments

Taught in English

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This course is part of the Genomic Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 4 modules in this course

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.

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.

What's included

21 videos3 readings1 assignment

21 videosTotal 129 minutes

Welcome to Statistics for Genomic Data Science3 minutes
What is Statistics?3 minutes
Finding Statistics You Can Trust (4:44)5 minutes
Getting Help (3:44)4 minutes
What is Data? (4:28)4 minutes
Representing Data (5:23)5 minutes
Module 1 Overview (1:07)1 minute
Reproducible Research (3:42)4 minutes
Achieving Reproducible Research (5:02)5 minutes
R Markdown (6:26)6 minutes
The Three Tables in Genomics (2:10)2 minutes
The Three Tables in Genomics (in R) (3:46)4 minutes
Experimental Design: Variability, Replication, and Power (14:17)14 minutes
Experimental Design: Confounding and Randomization (9:26)9 minutes
Exploratory Analysis (9:21)9 minutes
Exploratory Analysis in R Part I (7:22)7 minutes
Exploratory Analysis in R Part II (10:07)10 minutes
Exploratory Analysis in R Part III (7:26)7 minutes
Data Transforms (7:31)8 minutes
Clustering (8:43)9 minutes
Clustering in R (9:09)9 minutes

3 readingsTotal 30 minutes

Syllabus10 minutes
Pre Course Survey10 minutes
Introduction and Materials10 minutes

1 assignmentTotal 30 minutes

Module 1 Quiz30 minutes

This week we will cover preprocessing, linear modeling, and batch effects.

What's included

14 videos1 assignment

14 videosTotal 97 minutes

Module 2 Overview (1:12)1 minute
Dimension Reduction (12:13)12 minutes
Dimension Reduction (in R) (8:48)9 minutes
Pre-processing and Normalization (11:26)11 minutes
Quantile Normalization (in R) (4:49)5 minutes
The Linear Model (6:50)7 minutes
Linear Models with Categorical Covariates (4:08)4 minutes
Adjusting for Covariates (4:16)4 minutes
Linear Regression in R (13:03)13 minutes
Many Regressions at Once (3:50)4 minutes
Many Regressions in R (7:21)7 minutes
Batch Effects and Confounders (7:11)7 minutes
Batch Effects in R: Part A (8:18)8 minutes
Batch Effects in R: Part B (3:50)4 minutes

1 assignmentTotal 30 minutes

Module 2 Quiz30 minutes

This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.

What's included

15 videos1 assignment

15 videosTotal 86 minutes

Module 3 Overview (1:07)1 minute
Logistic Regression (7:03)7 minutes
Regression for Counts (5:02)5 minutes
GLMs in R (9:28)9 minutes
Inference (4:18)4 minutes
Null and Alternative Hypotheses (4:45)5 minutes
Calculating Statistics (5:11)5 minutes
Comparing Models (7:08)7 minutes
Calculating Statistics in R10 minutes
Permutation (3:26)3 minutes
Permutation in R (3:33)4 minutes
P-values (6:04)6 minutes
Multiple Testing (8:25)8 minutes
P-values and Multiple Testing in R: Part A (5:58)6 minutes
P-values and Multiple Testing in R: Part B (4:23)4 minutes

1 assignmentTotal 30 minutes

Module 3 Quiz30 minutes

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.

What's included

14 videos1 reading1 assignment

14 videosTotal 74 minutes

Module 4 Overview (1:21)1 minute
Gene Set Enrichment (4:19)4 minutes
More Enrichment (3:59)4 minutes
Gene Set Analysis in R (7:43)8 minutes
The Process for RNA-seq (3:59)4 minutes
The Process for Chip-Seq (5:25)5 minutes
The Process for DNA Methylation (5:03)5 minutes
The Process for GWAS/WGS (6:12)6 minutes
Combining Data Types (eQTL) (6:04)6 minutes
eQTL in R (10:36)11 minutes
Researcher Degrees of Freedom (5:49)6 minutes
Inference vs. Prediction (8:52)9 minutes
Knowing When to Get Help (2:31)3 minutes
Statistics for Genomic Data Science Wrap-Up (1:53)2 minutes

1 readingTotal 10 minutes

Post Course Survey10 minutes

1 assignmentTotal 30 minutes

Module 4 Quiz30 minutes

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Instructor

Instructor ratings

(38 ratings)

Jeff Leek, PhD

Johns Hopkins University

32 Courses1,760,828 learners

Offered by

Johns Hopkins University

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Reviewed on Feb 12, 2017

Overall, a very good course. Not without its flaws (inconsistent video audio levels), but I have walked away knowing far more about Genomic Data Science than I expected to.

Reviewed on Jul 16, 2019

It is really great that told me lots of basic statistical information that I didn't know.

Reviewed on Feb 19, 2018

Enjoyed it. One of better courses I have taken in Coursera. A good introduction to using statistics in Bioconductor with genomics data.

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