Skip to main content

140.688.01
Statistics for Genomics

Location
East Baltimore
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2012 - 2013
Instruction Method
TBD
Class Time(s)
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Contact Name
Frequency Schedule
Every Year
Description
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.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. describe the basics of how microarray technology works
  2. critique existing methodology for the analysis of microarray data
  3. Write R code to import and analyze microarray data