Abstract: An apparatus and method for identifying clusters in two-dimensional data by generating a two-dimensional histogram characterized by a grid of bins, determining a density estimate based on the bins, and identifying at least one cluster in the data. A smoothed density estimate is generated using a Gaussian kernel estimator algorithm. Clusters are identified by locating peaks and valleys in the density estimate (e.g., by comparing slope of adjacent bins). Boundaries (e.g., polygons) around clusters are identified using bins after bins are identified as being associated with a cluster. Boundaries can be simplified (e.g., by reducing the number of vertices in a polygon) to facilitate data manipulation.

Abstract: The present invention provides instruments for analyzing a multiplicity of fluorescent dyes using a multiplicity of amplifying photodetectors, methods for using the instruments, methods for setting the instrument parameters, and methods for resetting the instrument parameters following a changed in photodetector amplification. The present invention is particularly applicable in the field of flow cytometry.

Abstract: The present invention provides instruments for analyzing a multiplicity of fluorescent dyes using a multiplicity of amplifying photodetectors, methods for using the instruments, methods for setting the instrument parameters, and methods for resetting the instrument parameters following a changed in photodetector amplification. The present invention is particularly applicable in the field of flow cytometry.

Abstract: An apparatus and method for identifying clusters in two-dimensional data by generating a two-dimensional histogram characterized by a grid of bins, determining a density estimate based on the bins, and identifying at least one cluster in the data. A smoothed density estimate is generated using a Gaussian kernel estimator algorithm. Clusters are identified by locating peaks and valleys in the density estimate (e.g., by comparing slope of adjacent bins). Boundaries (e.g., polygons) around clusters are identified using bins after bins are identified as being associated with a cluster. Boundaries can be simplified (e.g., by reducing the number of vertices in a polygon) to facilitate data manipulation.

Abstract: A method for automatically classifying multi-parameter data into cluster groups for the purpose of defining different populations of particles in a sample by automatically defining a position of at least one variable position, geometric boundary surface on a two-dimensional scatter plot so as to enclose a group of the displayed particles in a data cluster; with the boundary surface having a polygonal shape defined by a plurality of vertices about at least one cell cluster created by building at least one histogram from cross sections of the two-dimensional scatter plot. Preferably, each cross section of the geometric boundary includes a rectangular, two dimensional gate. The method is particularly useful in the field of cellular analysis using, for example, flow cytometers wherein multi-parameter data is recorded for each cell that passes through an illumination and sensing region.