Computational Functional Genomics

(Statistical Models in Computational Biology)

Links

Nadon and Shoemaker paper (please read)

Benjamini and Hochberg FDR paper

Smyth and Speed: Normalization of cDNA Microarray Data

Limma Description

Smyth-Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (you will need to "register" to download the paper for free)

(Statistical Models in Computational Biology)

26-BE-790

Instructor: Mario Medvedovic

Instructor: Mario Medvedovic

**
Motivation:***
* The very nature of functional genomics research has undergone
major changes in the last 5 to 6 years. The changes were driven by the
development of high-throughput technologies for measuring levels of
bio-molecules on the genomic scale and by the development of easily
accessible databases containing a tremendous amount of accumulated
information about genomic sequences, protein sequences, functional
annotations, etc. Current functional genomics research depends heavily on
computational tools which allow access to various information depositories
as well as tools for mathematical and statistical modeling of data
obtained by modern experimental technologies such as DNA microarrays, 2D
gels, mass spectrometry, etc. While an increasing number of computational
tools are being developed that allow for analyzing such data, the
appropriate use of these tools depend on one’s understanding of the
underlying mathematical and statistical models. Furthermore, many of the
newly developed mathematical and statistical approaches are not supported
by an appropriate computational tool, but can be easily implemented by
using one of several widely available generic computational software
packages such as SAS, Splus, MATLAB, R, etc. Unlike the statistical tools
required for analyzing data generated by classical experimental approaches
that assessed only a few entities at a time, the analytical methods used
for large scale functional genomics data need to deal with additional
issues of multiple hypotheses testing, high-dimensional models, assessing
the statistical confidence in patterns discovered by data mining
techniques, etc. Human intuition, when not aided by formal mathematical
analysis, breaks down in such situations. This makes it imperative that
future generations of biomedical researchers acquire an understanding of
the mathematical and statistical methods underlying the tools they use to
analyze their data.

*Objectives***:***
* The goal of the course is to introduce students to statistical
models and concepts corresponding computational tools for analysis of
microarray data. Molecular biology students are expected to learn
principles upon which computational tools used in the analysis of
functional genomics (e.g. microarray) data are based. They are also
expected to gain a basic level understanding of how to write simple
programs in R which and make use of analytical procedures within the
Bioconductor package that are specifically developed for the analysis of
microarray data. Students with quantitative backgrounds are expected to
learn basic concepts of molecular genetics and specifics of applying
familiar concepts and tools to modeling functional genomics data.

Previous Editions: 2005

Teaching Assistants:

Johannes Freudenberg:
Johannes.Freudenberg@cchmc.org

Junhai Guo: guojs@email.uc.edu

Lectures from the Winter
quarter 2005/2006:

- Lecture 1/3/2006
- Lecture 1/5/2006
- Lecture 1/9/2006
- Lecture 1/12/2006
- Lecture 1/17/2006
- Lecture 1/19/2006
- Lecture 1/24/2006
- Lecture 1/26/2006
- Lecture 1/31/2006
- Lecture 2/7/2006
- Lecture 2/9/2006
- Lecture 2/14/2006
- Lecture 2/16/2006
- Lecture 2/23/2006
- Lecture 3/2/2006
- Lecture 3/7/2006

R-programs:

Links

Nadon and Shoemaker paper (please read)

Benjamini and Hochberg FDR paper

Smyth and Speed: Normalization of cDNA Microarray Data

Limma Description

Smyth-Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (you will need to "register" to download the paper for free)