1. A method for analysis and classification of flow cytometry data, the method comprising: downloading an input dataset comprising a plurality of assays performed on a plurality of samples by flow cytometry analysis into a computer system comprising a processor and a storage device, wherein the processor is programmed to execute at least one support vector machine and performs the steps of: pre-processing a first portion of the input dataset with one or more feature selection algorithm to select a subset of assays from the plurality of assays; training a support vector machine comprising a distributional kernel to separate the first portion of the input dataset corresponding to the selected subset of assays into two probability distributions and measuring a discrepancy between the two probability distributions; testing the trained support vector machine with a second portion of the input dataset using the selected feature subset to determine whether an optimal solution is achieved; if an optimal solution is not achieved, repeating the steps of training and testing until the optimal solution is reached; once an optimal solution is reached, inputting a live data set of flow cytometry data into the computer system; processing the live data set using the selected subset of assays using the trained and tested support vector machine to produce a result comprising a classification of the flow cytometry data into one of two distinct classes; and generating an output display at a display device with an identification of a flow cytometry data classification.