University of Minnesota

School of Public Health

Division of Biostatistics

PubH 7445 Statistics for Human Genetics and Molecular Biology - Fall 2018

 

 

Syllabus and Course Information

 

 

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Instructor:

Dr. Cavan Reilly

cavanr@biostat.umn.edu

Teaching Assistant:

TBD

Office hours for Cavan: Mon. and Wed. 2:30-3:30 in the Coordinating Centers for Biometric Research (2221 University Ave, Floor 2) (612.624.9644)

Office hours for the TA: NA, there is no TA for this course this year


Week

Topics

Lecture Notes

R Code

Homework

Reading

1

Review of Molecular Biology

Review of biology

Homework 1

SimpleR section 1-3 (Introduction, Data, Univariate Data), Foulkes Chapter 1, SimpleR section 6 (Random Data), Appendix on t-tests (page 97-99))

2

Review of Statistics and Introduction to R

Review of Statistics part 1

Review of Statistics part 2

An introduction to R

Lab 1

Homework 2

SimpleR Section 4, 5, 7, 8, 10, 11, 12, 13, 15, Foulkes Chapter 2

failrate data set

3

Linkage disequilibrium, Hardy Weinberg equilibrium

Foulkes Chapter 1

Foulkes Chapter 3

Lab 2

Lab 3

Homework 3

Foulkes Chapter 3

Handout on Reporting

Different kinds of tests

A latex header

Handout on population structure

4

Multiple comparisons for GWAS

Foulkes Chapter 4

Lab 4

Homework 4

Foulkes Chapter 4

5

Testing associations between phenotypes and haplotypes

Foulkes Chapter 5

Lab 5

Part A 5.2

Homework 5

Foulkes Chapter 5

6

Supervised learning with high dimensional data and categorical predictors

Foulkes Chapter 6

Lab 6

Homework 6

Foulkes Chapters 6 and 7

7

More on supervised learning

Foulkes Chapter 7 SVMs

Lab 7

Comparing Classifiers

8

Introduction to microarray analysis

Introduction to Microarrays

Genomic Data Structures

Lab 8

Bioconductor Case Studies Chapters 1-4

9

Differential expression

Processing Affymetrix Arrays

Lab 9

Bioconductor Case Studies Chapters 6-7

10

Interpretation of differentially expressed genes

Annotation

Lab 10

Homework 7

Bioconductor Case Studies Chapter 8

11

Next generation sequencing: bioinformatics

Overview

BWT Example

Introduction to Linux

computer commands

Cufflinks test data

12

Next generation sequencing: biostatistics

13

Machine learning approaches to gene expression data

14

R tools for pathway analysis using RNA-Seq data

15

Proteomics and metabolomics