When examining one or higher dimensional data, researchers frequently aim to identify individual subsets (clusters) of objects within the dataset. With high-dimensional data (>3 dimensions), the data become progressively more sparsly distributed in space.
The technologies described in this patent address a critically important deficit in the statistical methods available to enable comparison of outcomes measured by flow cytometry or similar, data intensive technologies.
Researchers in Dr. Leonore Herzenberg's lab at Stanford University have developed a portfolio of data management, storage, and analysis technologies that may be used for large data sets.
This patented, automated data analytics tool sorts and analyzes large data sets by identifying and creating clusters of data. The algorithm intakes data and then groups them into clusters, groupings, or populations of data.