Course Summary
This course introduces statistical methods as applied to genomic data analysis. Topics include multiple testing, false discovery rates, Bayesian methods, and machine learning techniques.Key Learning Points
- Learn how to apply statistical methods to genomic data analysis
- Understand multiple testing, false discovery rates, Bayesian methods, and machine learning techniques
- Gain practical experience through hands-on exercises and case studies
Related Topics for further study
Learning Outcomes
- Understand the principles of statistical genomics
- Apply statistical methods to genomic data analysis
- Interpret results and draw conclusions from genomic data analysis
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with genomics and molecular biology
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Bioinformatics
- Introduction to Computational Biology
- Data Science in Genomics
Related Education Paths
- Master of Science in Bioinformatics
- Master of Science in Biostatistics
- Certificate in Genomics and Precision Medicine
Notable People in This Field
- Eric Lander
- Francis Collins
- Mary-Claire King
Related Books
Description
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.
Outline
- Module 1
- Welcome to Statistics for Genomic Data Science
- What is Statistics?
- Finding Statistics You Can Trust (4:44)
- Getting Help (3:44)
- What is Data? (4:28)
- Representing Data (5:23)
- Module 1 Overview (1:07)
- Reproducible Research (3:42)
- Achieving Reproducible Research (5:02)
- R Markdown (6:26)
- The Three Tables in Genomics (2:10)
- The Three Tables in Genomics (in R) (3:46)
- Experimental Design: Variability, Replication, and Power (14:17)
- Experimental Design: Confounding and Randomization (9:26)
- Exploratory Analysis (9:21)
- Exploratory Analysis in R Part I (7:22)
- Exploratory Analysis in R Part II (10:07)
- Exploratory Analysis in R Part III (7:26)
- Data Transforms (7:31)
- Clustering (8:43)
- Clustering in R (9:09)
- Syllabus
- Pre Course Survey
- Introduction and Materials
- Module 1 Quiz
- Module 2
- Module 2 Overview (1:12)
- Dimension Reduction (12:13)
- Dimension Reduction (in R) (8:48)
- Pre-processing and Normalization (11:26)
- Quantile Normalization (in R) (4:49)
- The Linear Model (6:50)
- Linear Models with Categorical Covariates (4:08)
- Adjusting for Covariates (4:16)
- Linear Regression in R (13:03)
- Many Regressions at Once (3:50)
- Many Regressions in R (7:21)
- Batch Effects and Confounders (7:11)
- Batch Effects in R: Part A (8:18)
- Batch Effects in R: Part B (3:50)
- Module 2 Quiz
- Module 3
- Module 3 Overview (1:07)
- Logistic Regression (7:03)
- Regression for Counts (5:02)
- GLMs in R (9:28)
- Inference (4:18)
- Null and Alternative Hypotheses (4:45)
- Calculating Statistics (5:11)
- Comparing Models (7:08)
- Calculating Statistics in R
- Permutation (3:26)
- Permutation in R (3:33)
- P-values (6:04)
- Multiple Testing (8:25)
- P-values and Multiple Testing in R: Part A (5:58)
- P-values and Multiple Testing in R: Part B (4:23)
- Module 3 Quiz
- Module 4
- Module 4 Overview (1:21)
- Gene Set Enrichment (4:19)
- More Enrichment (3:59)
- Gene Set Analysis in R (7:43)
- The Process for RNA-seq (3:59)
- The Process for Chip-Seq (5:25)
- The Process for DNA Methylation (5:03)
- The Process for GWAS/WGS (6:12)
- Combining Data Types (eQTL) (6:04)
- eQTL in R (10:36)
- Researcher Degrees of Freedom (5:49)
- Inference vs. Prediction (8:52)
- Knowing When to Get Help (2:31)
- Statistics for Genomic Data Science Wrap-Up (1:53)
- Post Course Survey
- Module 4 Quiz
Summary of User Reviews
Statistical Genomics is a highly recommended course for those interested in learning about the intersection of genetics and statistics. Users have praised the course's engaging content and knowledgeable instructors.Key Aspect Users Liked About This Course
The instructors are knowledgeable and engaging.Pros from User Reviews
- The content is well-organized and easy to follow.
- The instructors provide clear explanations and examples.
- The course covers a variety of topics related to statistical genomics.
- The assignments are challenging but manageable.
- The course provides a good foundation for further study in the field.
Cons from User Reviews
- Some users found the pace of the course to be too slow.
- A few users felt that the course could benefit from more hands-on exercises.
- Some users were disappointed that the course did not go into more depth on certain topics.
- A few users felt that the course was too basic for those with a background in genetics or statistics.
- A handful of users experienced technical difficulties with the Coursera platform.
Jeff Leek, PhD
4.2 Raiting
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