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Activity Number: 428
Type: Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #305524
Title: Distributional Properties of Digital Gene Expression Data
Author(s): Lisa M. Chung*+ and Michael A. Newton
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
Address: 4706 Regent St. #47B, Madison, 53705,
Keywords: Digital gene expression ; tag-based sequencing methods ; gene expression ; differential gene expression
Abstract:

Digital gene expression technologies measure absolute abundances of transcripts by generating short tags and recording their frequencies, rather than by hybridization. Currently, several tag-based sequencing methods are available including serial analysis of gene expression (SAGE), RNA-Seq, and digital gene expression - tag profiling (DGE). To identify whether or not a gene is differentially expressed between two classes, it is important to understand distributional characteristics of the data. To do so a set of replicated DGE profiles are examined. Further, a two-step DE analysis approach is proposed.

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