Field
The present invention is generally directed to displaying and analyzing data, and more particularly directed to displaying and analyzing data from biological sample analyzers, such as flow cytometer instruments.
Background
Many researchers need to analyze and plot large amounts of data, e.g., multidimensional data. For example, a system which generates large amounts of data may be a biological sample analyzer, such as a flow cytometer instrument. Flow cytometers are widely used for clinical and research use. A biological mixture may comprise a fluid medium carrying a biological sample such as a plurality of discrete biological particles, e.g., cells, suspended therein. Biological samples can include blood samples or other cells within a heterogeneous population of cells. Information obtained from the biological particles is often used for clinical diagnostics and/or data analyses.
Flow cytometry is a technology that is used to simultaneously measure and analyze multiple parameters (e.g., physical characteristics or dimensions) of particles, such as cells. Flow cytometry analysis includes techniques for analyzing multiple parameters. Parameters (e.g., characteristics, properties, and dimensions) measurable by flow cytometry include cellular size, granularity, internal complexity, fluorescence intensity, and other features. Some parameters may be measurable after adding a marker. For example, fluorochrome-conjugated antibodies may emit photons of light in an identifiable spectrum upon excitation of the fluorochrome. Detectors are used to detect forward scatter, side scatter, fluorescence, etc. in order to measure various cellular properties. Cellular parameters identified by flow cytometer instruments can then be used to analyze, identify, and/or sort cells.
In traditional flow cytometry systems, a flow cytometer instrument is a hardware device used to pass a plurality of cells singularly through a beam of radiation formed by a light source, such as a laser beam. A flow cytometer instrument captures light that emerges from interaction(s) with each of the plurality of cells as each cell passes through the beam of radiation.
Currently available flow cytometry systems may include three main systems, i.e., a fluidic system, an optical system, and an electronics system. The fluidic system may be used to transport the particles in a fluid stream past the laser beam. The optical system may include the laser that illuminates the individual particles in the fluid stream, optical filters that filter the light before or after interacting with the fluid stream, and the photomultiplier tubes that detect the light beam after the light passes through the fluid stream to detect, for example, fluorescence and/or scatter. The electronic system may be used to process the signal generated by the photomultiplier tubes or other detectors, convert those signals, if necessary, into digital form, store the digital signal and/or other identification information for the cells, and generate control signals for controlling the sorting of particles. The data point having the parameters corresponding to the measurement of one cell or other particle is termed an event. In traditional flow cytometry systems, a computer system converts signals received from detectors such as light detectors into digital data that is analyzed.
Flow cytometry systems capture large amounts of data from passing thousands of cells per second through the laser beam. Subpopulations of captured flow cytometry data must be selected and gated (e.g., by drawing a gate on a displayed graph or plot) so that statistical analysis can subsequently be performed on the data. Since flow cytometers operate at very high speeds and collect large amounts of data in short amounts of time, it is necessary for the data display and analysis systems to operate at very high speeds and to graphically depict the data efficiently. Statistical analysis of the data can be performed by a computer system running software that generates reports on the characteristics of selected subpopulations (i.e., gates) of the cells, wherein the cellular characteristics include one or more of cellular size, mitotic phase, cellular complexity, phenotype, and health.
Many conventional flow cytometry systems depict data as series of individual scatter plots (i.e., dot plots) or histograms. Two dimensional dot plots are not well suited for near instantaneous analysis and display of large amounts of data. Although many report-writing tools exist for polychromatic flow cytometry data, these traditional tools do not allow users to interactively display hierarchical, iterative tree plots that summarize large flow cytometry data sets. Accordingly, what is needed are methods, systems, and computer program products that allow users to alter and fine-tune graphs depicting flow cytometry data interactively, dynamically adjusting views of the data, even in cases where the graphs represent large amounts of data.
Traditional flow cytometry analysis tools do not allow users to interactively alter plots representing flow cytometry data on an ad-hoc basis such that the plots are updated substantially immediately. Flow cytometry list mode files are files containing raw flow cytometry data, such as FCS files. As used herein, an FCS file refers to a flow cytometry data file compliant with the International Society for Advancement of Cytometry (ISAC) Flow Cytometry Standard (FCS).
There are technical challenges involved in analyzing and graphically depicting large amounts of Polychromatic Flow Cytometry data. In traditional systems, as flow cytometry datasets increase in size, there is a corresponding degradation in data display and statistical analysis performance.
Flow cytometry systems capture large numbers of events from passing thousands of cells per second through the laser beam. Captured flow cytometry data is stored so that statistical analysis can subsequently be performed on the data. Typically, flow cytometers operate at high speeds and collect large amounts of data. Statistical analysis of the data can be performed by a computer system running software that generates reports on the characteristics (i.e., dimensions) of the cells, such as cellular size, complexity, phenotype, and health. Polychromatic flow cytometry refers to methods to analyze and display complex multi-parameter data from a flow cytometer instrument. Polychromatic flow cytometry data may include many parameters. Conventional flow cytometry systems depict this data as series of graphs, such as scatter plots and histograms, to aid operator analysis of the data. These conventional flow cytometry systems encounter difficulties efficiently depicting polychromatic flow cytometry data containing 6 or more colors. These conventional systems also do not allow users, such as researchers, flow cytometrists, and clinicians to interact with the scatter plots and histograms in order to select subpopulations or ‘gates’ of data to be depicted in new and updated interactive plots which are substantially immediately generated and updated.
Scatter plots and histograms are the common visualization and analysis tools used by flow cytometrists and clinicians. The number of bivariate scatter plots that can be generated for a cytometry protocol with N fluorochromes is (N×(N−1))/2 and the number of univariate plots is N. If, for example, a protocol has 5 fluorochromes, then the number of scatter plots that can be generated is (5×4)/2 or 10. When 18 color protocols are used (18×17)/2 or 153 scatter plots can be generated. Flow cytometrists, researchers, and clinicians experience difficulties assimilating and analyzing information from large numbers of scatter plots. For example, it is difficult for users to readily identify biologically significant events within 153 scatter plots.
There are difficulties and challenges associated with displaying, visualizing, and analyzing polychromatic flow cytometry data. These challenges increase with data generated by 6 or more fluorochromes. Traditional analysis and display tools do not readily reveal the biological significance of event data in a manner that allows users and clinicians to iteratively update a related set of interactive plots. Accordingly, what is needed are methods and systems that allow display and analysis of large amounts of polychromatic flow cytometry data.