1. A computer implemented method for creating a decision model for classifying a biological data set produced by mass spectroscopy analysis, wherein said classifying comprises determining whether said biological data set is in a first state or a second state, said computer implemented method comprising: inputting a first training set of data into a computer, a plurality of said first set each derived from biological samples of a first state, said computer comprising at least a processor, data storage, an input module, and an output module; inputting a second training set of data into a said computer, a plurality of said second set each derived from biological samples of a second state; aligning and normalizing said first training set and said second training set using an image processing algorithm executing on said computer and storing in memory first and second aligned and normalized training images; deriving a differential image mask by comparing said first and second aligned and normalized training images using an image processing algorithm executing on said computer system, thereby determining image pixel locations that are not useful in distinguishing between said first set and said second set and storing results as a differential image mask in said computer; applying said differential image mask to generate a filtered first set and a filtered second set of data images using an image processing algorithm executing on said computer system; wherein a plurality of said images comprise an array of pixel values; expressing said filtered first set and second set of data images as a first set and second set of vectors in a basis of most significant principal components of said pixel values' covariance matrix, wherein said matrix expresses covariance between pixels' expression levels across said filtered first set and said filtered second set of data images and storing said first set and second set of vectors in said computer; using said computer system to select a decision procedure based on relative distribution and separability of said first set and second set of vectors; applying said decision procedure to an unclassified data image to determine if said image is in said first state or said second state; outputting a result indicating whether unclassified data image is in said first state or said second state.