Harnessing Crowd-Sourcing to Select Genes Based on Effect Size Using Visual Inference Methods

• Activity Number: 2
• Type: Invited
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Graphics
• Author(s): Dianne Cook*+
• Companies: Iowa State University
• Keywords: visualization ; data mining ; statistical graphics ; bioinformatics ; RNA-Seq

Abstract

The volume of high-throughput data makes it a daunting prospect to plot, but relying primarily on false discovery rate adjusted p-values is not enough. Making plots of the data is essential to diagnose the models and understand the results. New work on inference for statistical graphics is used to examine high-throughput data. Initial validation experiments suggest that the results obtained reflect the strength of the effect size or signal in the data better than conventional p-values can capture. This has the potential to solve inconsistencies in results from different analysis methods and between choices made on the basis of fold change versus p-value. Crowd-sourcing is done using Amazon's Mechanical Turk, and data is drawn from RNA-Seq experiments on soybean plants.