Bioinformatics and Computational Biology Solutions Using R and Bioconductor

• Author: Irizarry, Rafael
• Subject: Science and Technology, Social Sciences, Mathematics and Statistics
• Institution Name: Johns Hopkins Bloomberg School of Public Health
• Collection: JHSPH OpenCourseWare
• Grade Level: Post-secondary

• Abstract: Covers the basics of R software and the key capabilities of the Bioconductor project (a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology and rooted in the open source statistical computing environment R), including importation and preprocessing of high-throughput data from microarrays and other platforms. Also introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of of preprocessing and normalization, statistical inference, multiple comparison corrections, Bayesian Inference in the context of multiple comparisons, clustering, and classification/machine learning.

• Course Type: Full Course
• Languages: English
• Material Types: Lecture Notes, Activities and Labs, Syllabi
• Media Formats: Text/HTML, Downloadable docs, Graphics/Photos
• Technical Requirements: Adobe Acrobat

• Conditions of Use: Creative Commons Attribution-Noncommercial-Share Alike 2.5