Statistics for Genomic Data Science

Statistics for Genomic Data Science

This course is part of Genomic Data Science Specialization

Instructor: Jeff Leek, PhD

Access provided by PALC Dev

40,133 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

378 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.

378 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

Add to your LinkedIn profile

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 videos Total 129 minutes

Welcome to Statistics for Genomic Data Science 3 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 readings Total 30 minutes

Syllabus 10 minutes
Pre Course Survey 10 minutes
Introduction and Materials 10 minutes

1 assignment Total 30 minutes

Module 1 Quiz 30 minutes

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

What's included

14 videos1 assignment

14 videos Total 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 assignment Total 30 minutes

Module 2 Quiz 30 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 videos Total 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 R 10 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 assignment Total 30 minutes

Module 3 Quiz 30 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 videos Total 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 reading Total 10 minutes

Post Course Survey 10 minutes

1 assignment Total 30 minutes

Module 4 Quiz 30 minutes

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Instructor

Instructor ratings

(38 ratings)

Jeff Leek, PhD

Johns Hopkins University

32 Courses 1,748,535 learners

Offered by

Johns Hopkins University

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Reviewed on Aug 7, 2020

Very helpful and i understood i should master statistics and do more research

Reviewed on Apr 8, 2021

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

Reviewed on Feb 11, 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.

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