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Description of db-iGA: To determine which proteins had coenriched with known vacuolar proteins, a modified version of the iterative group analysis (iGA) algorithm is implemented here. For each localization class the list of ranked items is analyzed using the hypergeometric statistics as described previously (1) using all possible windows to define groups. The window that shows the most surprising clustering of proteins from the same localization class, which is the highest probability of change (_log(PC)) value as defined in Breitling et al. (1), is recorded. This procedure, which is called double boundary iGA, is more flexible than the original iGA approach, which only tests windows at the extremes (top or bottom) of the list and would for example miss clustering in the middle. Multiple testing-corrected p values can be determined using random permutations of the item list for each class.

Requirement: Mathematica 6.0 or higher (not tested with earlier versions)

Using the implementation: User-defined variables must be set in order to perform the analysis. These variables are defined at the top of the code in the file db-iga.m.

Input file: Requires a ranked list of your groups in a comma separated format, where row -> rank, column -> annotation Example: 1, vacuole 2, vacuole, golgi 3, golgi, PM

Output of db-iGA: The program creates two different types of outputs, 1) A Text output with the list of all PC values for the type of analysis performed. 2) Plots the values in 2d (top boundary fixed IGA) and 3d (for dbl IGA analyses), 3) Permutation analysis for dbl-IGA

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