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Statistics for Genomics

East Baltimore
4th term
3 credits
Academic Year:
2013 - 2014
Instruction Method:
Class Times:
  • Tu Th,  1:30 - 2:50pm
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Kasper Hansen

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
Methods of Assessment:

Student evaluation is based on data analysis homework assignments and a final project. Students who want to learn the concepts without programming may take the class pass/fail and perform a literature review for a final project.

Instructor Consent:

No consent required