Introduction

We begin with an introduction to the biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.

<a href="http://www.youtube.com/watch?v=nwFyIxc4AE0" rel="nofollow" target="_blank"><img src="http://img.youtube.com/vi/nwFyIxc4AE0/0.jpg" alt="0" title="How To Choose The Correct Channel Type For Your Video Content " /></a>

What you'll learn:

  • Introduction to high-throughput technologies
    • Next Generation Sequencing
    • Microarrays
  • What we measure with high-throughput technologies and why
  • Preprocessing and Normalization
  • The Bioconductor Genomic Ranges Utilities
  • Genomic Annotation

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics


This class was supported in part by NIH grant R25GM114818.

 

Meet The Faculty

Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics, T.H. Chan School of Public Health

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

Michael Love

Michael Love

Postdoctoral Fellow, Harvard T.H. Chan School of Public Health

Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health. Dr. Love received his bachelor’s in mathematics in 2005 from Stanford University, his master’s in statistics in 2010 from Stanford University, and his Ph.D. in Computational Biology in 2013 from the Freie Universität Berlin. Dr. Love uses statistical models to infer biologically meaningful patterns from high-throughput sequencing data, and develops open-source statistical software for the Bioconductor Project.

Vincent Carey

Vincent Carey

Associate Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School

Vincent Carey is Associate Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School. As a Fulbright Specialist and as an invited lecturer, he has given short courses in statistical genomics on four continents. He was an inaugural faculty member in the Cold Spring Harbor Laboratory Summer Course on statistical analysis of genome-scale data, and is former Editor-in-Chief of The R Journal. He is Scientific Director of Bioinformatics in the National Institute of Allergy and Infectious Diseases Immune Tolerance Network, and is a member of the Scientific Advisory Board of the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery. Vince is a co-founder of the Bioconductor project.

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