Method for detecting discriminatory data patterns in multiple sets of data and diagnosing disease

A method of detecting cancer from mass spectrometer data of biological samples, comprising:

• for at least a portion of the biological samples, perform the steps of:
• ionizing the biological sample to produce ions;
• and
• detecting the ions by use of a mass spectrometer, to produce mass spectrometer data;
• the method further comprising the steps of:
• normalizing and smoothing the mass spectrometer data, to produce standardized and smoothed data;
• randomly sampling data that has been standardized and smoothed to divide the standardized and smoothed data into a training set of mass spectrometer data and a testing set of mass spectrometer data, each of the training set and the testing set comprising random samples from subjects affected and unaffected by the disease;
• performing a point-wise test on the training set of mass spectrometer data to determine test statistic values indicative of the difference between corresponding mass spectrometer data values of the samples of the affected and the unaffected subjects;
• determining a threshold test statistic value using a multiple-test correction method based on the size of the training set of mass spectrometer data and a selected significance level;
• selecting candidate markers having mass spectrometer data values, the mass spectrometer data values having a test statistic value with an absolute value that exceeds the threshold;
• selecting a subset of markers from the candidate markers using a best k-subset discriminant method to discriminate between the affected and the unaffected samples of the training set;
• and
• classifying the testing set of mass spectrometer data as representing affected or unaffected samples using the subset of markers;
• and
• outputting the result of the classifying step to a computer readable medium,
• wherein at least one candidate marker has a test statistic value with an absolute value that exceeds the threshold test statistic value.