Introduction

In this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.

<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:

  • Organizing high throughput data
  • Multiple comparison problem
  • Family Wide Error Rates
  • False Discovery Rate
  • Error Rate Control procedures
  • Bonferroni Correction
  • q-values
  • Statistical Modeling
  • Hierarchical Models and the basics of Bayesian Statistics
  • Exploratory Data Analysis for High throughput data

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.

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