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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

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 Science2 minutesPreview module
What is Statistics?2 minutes
Finding Statistics You Can Trust (4:44)4 minutes
Getting Help (3:44)3 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)3 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)3 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)7 minutes
Clustering (8:43)8 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 minutePreview module
Dimension Reduction (12:13)12 minutes
Dimension Reduction (in R) (8:48)8 minutes
Pre-processing and Normalization (11:26)11 minutes
Quantile Normalization (in R) (4:49)4 minutes
The Linear Model (6:50)6 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)3 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)3 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 85 minutes

Module 3 Overview (1:07)1 minutePreview module
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)4 minutes
Calculating Statistics (5:11)5 minutes
Comparing Models (7:08)7 minutes
Calculating Statistics in R9 minutes
Permutation (3:26)3 minutes
Permutation in R (3:33)3 minutes
P-values (6:04)6 minutes
Multiple Testing (8:25)8 minutes
P-values and Multiple Testing in R: Part A (5:58)5 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.