The University of Texas at Austin
Center for Biomedical Research Support

Author Of 1 Presentation

Biostatistical Methods Poster Presentation

P0001 - A new software and analysis suite for experimental autoimmune encephalomyelitis (ID 831)

Speakers
Presentation Number
P0001
Presentation Topic
Biostatistical Methods

Abstract

Background

Experimental Autoimmune Encephalomyelitis (EAE) is the most widely used multiple sclerosis animal model. In this model, animals develop an autoimmune inflammatory immune response to myelin protein leading to the development of ascending paralysis. The degree of motor dysfunction is scored daily and used as a metric of disease progression referred to as EAE disease score. Despite the common use of EAE animal models, there is still a large degree of heterogeneity in the field on EAE disease score data presentation and statistical assessment, making it challenging to compare results across studies.

Objectives

In an effort to standardize EAE disease score assessment, our objective was to develop an easy to use web-based application and associated package that provides researchers with a user interface for graphing and conducting statistical analysis of their data.

Methods

Using the programming language R, a shiny based application was developed to provide users with an interface to process their EAE data. For statistical testing, the application was flexibly designed to allow for parametric and non-parametric testing paradigms in addition to a number of different p-value correction methods.

Results

Our application uses a multifaceted analysis including EAE score curves (graphing EAE disease scores by group over time), area under the curve, Poisson modeling of frequency of days over a score threshold, and hierarchical clustering with a unique option of aligning animals by the day of disease onset. With this tool, researchers can quickly analyze their EAE data without code from the user while still maintaining a wide degree of flexibility in graphic and models parameters.

Conclusions

We have developed an application and an accompanying package that can be used quickly and easily to generate results for EAE disease score analysis. In the application, a user-friendly interface designed specifically for EAE models helps researchers assess their data by auto-generating graphs and statistical tests in a fast reproducible manner. We hope that this application and its analyses will assist researchers while initiating discussion and moving towards a field standard of EAE data presentation and analysis.

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