Workshop:

Weighted Gene Co-Expression Network Analysis with Applications in Neuroscience and Systems Biology

Confirmed Speakers:

Steve Horvath, University of California, Los Angeles

Jeremy Miller, Allen Brain Institute

Mike Hawrylycz, Allen Brain Institute

Time: Feb 22, 2012, 8:30am-5:00pm

Location: Allen Institute, TBA


Abstract
Computational networks analysis methods have been increasingly applied to analyze a variety of high dimensional data type including microarray and RNA-seq, methylation and other epigenetic data, as well as proteomics, and more recently fMRI. We present a one day workshop on state-of-the-art methods, software, and applications involving weighted network analysis. The workshop will also review data mining techniques and analysis strategies for high dimensional data. The workshop will be centered on computational methods in the R programming language, and will illustrate how to use these methods in systems biology and systems-genetic applications. The material should only require a basic knowledge of statistics. While not required, participants will have the option to follow material using their own laptops and the R language. The workshop is intended for students, faculty, and data analysts in fields including bioinformatics, computational biology, statistics, computer science, biology, genetics, applied mathematics, physics, and social science.

The following topics will be covered in detail:

· Methods for constructing gene networks based on gene expression data (microarray, RNA-seq), DNA methylation data and other high dimensional data

· Module detection algorithms and clustering procedures

· Differential network analysis

· Module preservation statistics

· Intramodular hub genes and module membership measures

· Network visualization methods (connectivity plots, MDS, VisANT)

· Gene enrichment analysis

Selected References

1) Horvath S (2011) Weighted Network Analysis: Applications in Genomics and Systems Biology. Springer Book. Hardcover ISBN: 978-1-4419-8818-8

2) Miller JA, Horvath S, Geschwind DH. (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A. 2010 Jul 13;107(28):12698-703. PMID: 20616000