Cyber-T is a statistics program with a web interface that can be conveniently used on high-dimensional array data for the identification of statistically significant differentially expressed genes. It employs statistical analyses based on simple t-tests that use the observed variance of replicate gene measurements across replicate experiments, or regularized t-tests that use a Bayesian estimate of the variance among gene measurements within an experiment. Cyber-T also contains a computational method (PPDE) for estimating experiment-wide false positive and negative levels based on the modeling of p-value distributions. [ Background of Cyber-T ]

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(2011-12-31) NEW VERSION of CyberT - We have recently made some large changes to CyberT. While most of the changes are simply adding functionality, some of the parameters have changed. If you have used CyberT before, the input parameters may have changed. Major additions include:

• A one-way ANOVA analysis using the Bayesian regularized variance estimates.
• Generalization of the framework to handle the extreme case of a single replicate in an experimental condition.
• Normalization methods, ability to use PPDE in all analyses, and other multiple hypothesis testing conditions.
• Example datasets and workflows for analysis of other high-throughput data (i.e., protein array or quantitative mass spectrometry data).