About 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.
Created by: Johns Hopkins University
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Basic Info | Course 7 of 8 in the Genomic Data Science Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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 videos, 3 readings
- Reading: Syllabus
- Reading: Pre Course Survey
- Reading: Introduction and Materials
- Video: What is Statistics?
- Video: Finding Statistics You Can Trust (4:44)
- Video: Getting Help (3:44)
- Video: What is Data? (4:28)
- Video: Representing Data (5:23)
- Video: Module 1 Overview (1:07)
- Video: Reproducible Research (3:42)
- Video: Achieving Reproducible Research (5:02)
- Video: R Markdown (6:26)
- Video: The Three Tables in Genomics (2:10)
- Video: The Three Tables in Genomics (in R) (3:46)
- Video: Experimental Design: Variability, Replication, and Power (14:17)
- Video: Experimental Design: Confounding and Randomization (9:26)
- Video: Exploratory Analysis (9:21)
- Video: Exploratory Analysis in R Part I (7:22)
- Video: Exploratory Analysis in R Part II (10:07)
- Video: Exploratory Analysis in R Part III (7:26)
- Video: Data Transforms (7:31)
- Video: Clustering (8:43)
- Video: Clustering in R (9:09)
Graded: Module 1 Quiz
WEEK 2
Module 2
This week we will cover preprocessing, linear modeling, and batch effects.
14 videos
- Video: Dimension Reduction (12:13)
- Video: Dimension Reduction (in R) (8:48)
- Video: Pre-processing and Normalization (11:26)
- Video: Quantile Normalization (in R) (4:49)
- Video: The Linear Model (6:50)
- Video: Linear Models with Categorical Covariates (4:08)
- Video: Adjusting for Covariates (4:16)
- Video: Linear Regression in R (13:03)
- Video: Many Regressions at Once (3:50)
- Video: Many Regressions in R (7:21)
- Video: Batch Effects and Confounders (7:11)
- Video: Batch Effects in R: Part A (8:18)
- Video: Batch Effects in R: Part B (3:50)
Graded: Module 2 Quiz
WEEK 3
Module 3
This week we will cover modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.
15 videos
- Video: Logistic Regression (7:03)
- Video: Regression for Counts (5:02)
- Video: GLMs in R (9:28)
- Video: Inference (4:18)
- Video: Null and Alternative Hypotheses (4:45)
- Video: Calculating Statistics (5:11)
- Video: Comparing Models (7:08)
- Video: Calculating Statistics in R
- Video: Permutation (3:26)
- Video: Permutation in R (3:33)
- Video: P-values (6:04)
- Video: Multiple Testing (8:25)
- Video: P-values and Multiple Testing in R: Part A (5:58)
- Video: P-values and Multiple Testing in R: Part B (4:23)
Graded: Module 3 Quiz
WEEK 4
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 videos, 1 reading
- Video: Gene Set Enrichment (4:19)
- Video: More Enrichment (3:59)
- Video: Gene Set Analysis in R (7:43)
- Video: The Process for RNA-seq (3:59)
- Video: The Process for Chip-Seq (5:25)
- Video: The Process for DNA Methylation (5:03)
- Video: The Process for GWAS/WGS (6:12)
- Video: Combining Data Types (eQTL) (6:04)
- Video: eQTL in R (10:36)
- Video: Researcher Degrees of Freedom (5:49)
- Video: Inference vs. Prediction (8:52)
- Video: Knowing When to Get Help (2:31)
- Video: Statistics for Genomic Data Science Wrap-Up (1:53)
- Reading: Post Course Survey
Graded: Module 4 Quiz
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