|
Activity Number:
|
416
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #309914 |
|
Title:
|
Analyzing Data from Gene Expression in Neuronal Activity
|
|
Author(s):
|
Xiaofeng Wang*+
|
|
Companies:
|
The Cleveland Clinic
|
|
Address:
|
Department of Quantitative Health Scie, Cleveland, OH, 44195,
|
|
Keywords:
|
Spatial count data ; Over-dispersion ; Simultaneous inference ; Neuronal gene expression ; Neuroscience
|
|
Abstract:
|
Neuronal gene expression is known to play a key role in determining the differentiation of neuronal development. An understanding of how neuronal activity modifies gene expression can help elucidate mechanisms of long-term neuronal plasticities in the brain. Data from gene expression in neuronal activity are commonly spatially-correlated counts of activated neurons. We discuss several models for the complex inhomogeneous count data: from parametrics to semiparametrics. Simultaneous confidence regions in these models are investigated. A real example in neuroscience are also explored in the paper.
|
- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
Back to the full JSM 2007 program |
