Documentation for TIGR Multiple Experiment Viewer (MeV)

Highlight groups of related proteins with Clustering.	Identify biomarkers with confidence using state of the art statistical tests with control of False Discovery Rate.	Select proteins that follow a given expression pattern with Pavlidis Template Matching.
Discover themes in expression profiles and assess image quality with Principal Component Analysis.	Go back and forth easily between statistics and image analysis. Review sets of relevant expression profiles.	Take advantage of state of the art analysis algorithms from the TIGR Multiple Experiment Viewer.

Delta2D 3.6 incorporates algorithms from the TIGR Multiple Experiment Viewer (MeV, version 4.0, tm4.org/mev.html, Saeed et al. 2003) and tightly integrates them into the image analysis workflow. With Delta2D's Complete Expression Profiles, there are no missing values, and matching problems are virtually eliminated. This makes Delta2D especially well suited for the methods that were originally applied in the context of DNA microarray analysis.