Statistics for Genomic Data Science

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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 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded…

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Didn't find what you were looking for? See also: Statistics, Science, Software / System Engineering, English (FCE / CAE / CPE), and Teaching Skills.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

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 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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 expand


  1. Video: Welcome to Statistics for Genomic Data Science
  2. Reading: Syllabus
  3. Reading: Pre Course Survey
  4. Reading: Introduction and Materials
  5. Video: What is Statistics?
  6. Video: Finding Statistics You Can Trust (4:44)
  7. Video: Getting Help (3:44)
  8. Video: What is Data? (4:28)
  9. Video: Representing Data (5:23)
  10. Video: Module 1 Overview (1:07)
  11. Video: Reproducible Research (3:42)
  12. Video: Achieving Reproducible Research (5:02)
  13. Video: R Markdown (6:26)
  14. Video: The Three Tables in Genomics (2:10)
  15. Video: The Three Tables in Genomics (in R) (3:46)
  16. Video: Experimental Design: Variability, Replication, and Power (14:17)
  17. Video: Experimental Design: Confounding and Randomization (9:26)
  18. Video: Exploratory Analysis (9:21)
  19. Video: Exploratory Analysis in R Part I (7:22)
  20. Video: Exploratory Analysis in R Part II (10:07)
  21. Video: Exploratory Analysis in R Part III (7:26)
  22. Video: Data Transforms (7:31)
  23. Video: Clustering (8:43)
  24. 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 expand


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


  1. Video: Module 3 Overview (1:07)
  2. Video: Logistic Regression (7:03)
  3. Video: Regression for Counts (5:02)
  4. Video: GLMs in R (9:28)
  5. Video: Inference (4:18)
  6. Video: Null and Alternative Hypotheses (4:45)
  7. Video: Calculating Statistics (5:11)
  8. Video: Comparing Models (7:08)
  9. Video: Calculating Statistics in R
  10. Video: Permutation (3:26)
  11. Video: Permutation in R (3:33)
  12. Video: P-values (6:04)
  13. Video: Multiple Testing (8:25)
  14. Video: P-values and Multiple Testing in R: Part A (5:58)
  15. 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 expand


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

Graded: Module 4 Quiz
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