The latest version of this course is available at https://mdozmorov.github.io/BIOS567.2017/. The lecture material is available at https://mdozmorov.github.io/BIOS567.2017/syllabus/

Required text: Sorin Draghici “Statistics and Data Analysis for Microarrays Using R and Bioconductor”, 2nd Ed., Chapman & Hall/CRC Press, 2012. ISBN-978-1-4398-0975-4, preview on Google books. Supplemental reading and course materials will be provided in-class

Software: The R programming environment

Course description

Welcome to BIOS 567. This course is the first part of the Genomics curriculum, aimed to introduce core principles of Bioinformatics, Computational Genomics and Biostatistics. This is a blended course that combines in-class learning (lectures, labs) with self-directed activities.

Bioinformatics is interdisciplinary computer science, requiring a knowledge base in biology, technology, and statistics. The operational definition of ‘bioinformatics’ for this course is “The application of biotechnology and statistical methods to the study of biological problems.” The biological problems on which we will focus are primarily gene expression studies conducted using microarray technology.

Course Objectives

• Gain insight into biological principles and the technology underlying microarrays, image analysis, normalization, and expression summary methods
• Learn practical Exploratory Data Analysis, visualization, and quality control
• Critically evaluate ant interpret statistical methods used in genomics data analysis
• Apply supervised and unsupervised methods to genomics data
• Assess statistical significance when multiple hypothesis tests are performed, such as in the analysis of differential gene expression
• Interpret biological findings obtained from statistical tests At the conclusion of the course, students will be able to collect, analyze and interpret genomics data using R programming environment

Course topics

• Reproducible research using R, Rmarkdown, tidyverse, GitHub
• Biological Background
• Microarray technology
• Image analysis
• Bioconductor overview
• Exploratory Data Analysis (EDA), Quality control
• Normalization, Expression summarization
• Differential expression
• Multiple hypothesis testing
• Supervised and Unsupervised learning, Clustering, dimensionaliry reduction
• Gene Ontology, Pathway and Functional enrichment
• Networks

This course on GitHub https://github.com/mdozmorov/bios567.2018

Acknowledgements