CN112955729B - Subsampling flow cytometry event data - Google Patents

Abstract

The present invention includes systems, devices, computer readable media, and methods for subsampling flow cytometry event data. The first and second flow cytometry event data may be converted into a low-dimensional space associated with the plurality of bins and may be assigned to the first bin and the second bin. The invention may generate subsampled flow cytometry event data comprising said first flow cytometry event data. The subsampled flow cytometry event data may include the second flow cytometry event data if the first bin is different from the second bin. The subsampled flow cytometry event data may not include the second flow cytometry event data if the first bin and the second bin are the same.

Description

Subsampling flow cytometry event data

Technical Field

The present invention relates generally to the field of automated particle assessment technology and, more particularly, to sample analysis and particle characterization methods.

Background Art

Particle analyzers (e.g., flow cytometers) can characterize particles based on electro-optical measurements such as light scattering and fluorescence. In a flow cytometer, for example, particles (e.g., molecules, microbeads bound to an analyte, or individual cells) in a fluid suspension pass through a detection region where they are exposed to excitation light, typically from one or more lasers, and the light scattering and fluorescence properties of the particles are measured. Particles or their components are typically labeled with fluorescent dyes for detection. Various different particles or components can be detected simultaneously by labeling with fluorescent dyes with different spectral properties. Different cell types can be identified by light scattering properties and fluorescence emissions obtained/generated by labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes. The data obtained by analyzing cells (or other particles) using multicolor flow cytometry is multidimensional, with each cell corresponding to a point in the multidimensional space defined by the measured parameters. Cell populations or particle populations are identified as clusters of points in the data space.

Summary of the invention

The present invention discloses a system, apparatus, computer readable medium and method for subsampling flow cytometry event data. In some embodiments, the method comprises: under the control of a processor: converting first flow cytometry event data associated with a first event in a first plurality of events in a flow cytometry event data set in a high-dimensional space into first converted flow cytometry event data associated with the first event in a first low-dimensional space. The first event may be associated with a positive subsampling demand. The first low-dimensional space may be associated with a first plurality of bins.

The first transformed flow cytometry event data may be associated with the first bin of the first plurality of bins. The method may include converting second flow cytometry event data associated with a second event of the first plurality of events in the flow cytometry event data set in the high-dimensional space into second transformed flow cytometry event data associated with the second event in the first low-dimensional space. The second event may be associated with the positive subsampling requirement. The second transformed flow cytometry event data may be associated with a second bin of the first plurality of bins. The method may include determining that the first bin associated with the first transformed flow cytometry event data and the second bin associated with the second transformed flow cytometry event data are different. The method may include generating a subsampled flow cytometry event data set of the flow cytometry event data, the data including the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

In some embodiments, the method may include: receiving flow cytometry event data including the first flow cytometry event data and the second flow cytometry event data. The method may include: determining that the first flow cytometry event data of the first event in the first plurality of events is associated with the positive subsampling requirement; and/or determining that the second flow cytometry event data of the second event in the first plurality of events is associated with the positive subsampling requirement. The method may include: determining that the first transformed flow cytometry event data is associated with the first bin in the first plurality of bins; and/or determining that the second transformed flow cytometry event data is associated with the second bin in the first plurality of bins. The method may include: determining a first descriptor of the first transformed flow cytometry event data based on the first bin in the first plurality of bins; and/or determining a second descriptor of the second transformed flow cytometry event data based on the second bin in the first plurality of bins. The first descriptor of the first transformed flow cytometry event data associated with the first bin may be a first bin number of the first bin in the first plurality of bins; and/or the second descriptor of the second transformed flow cytometry event data associated with the second bin may be a second bin number of the first bin in the first plurality of bins. The first flow cytometry event data may be associated with a first rare cell and/or the second flow cytometry event data may be associated with a second rare cell. The first rare cell and the second rare cell may be cells of different cell types.

The method may include: adding the first bin, the first descriptor and/or the first bin number to a memory data structure; and/or adding the second bin, the second descriptor and/or the second bin number to the memory data structure.

In some embodiments, the method includes converting third flow cytometry event data associated with a third event in the first plurality of events in the flow cytometry event data set in the high dimensional space into third transformed flow cytometry event data associated with the third event in the first low dimensional space. The third event may be associated with the positive subsampling requirement. The third transformed flow cytometry event data may be associated with the third bin in the first plurality of bins. The method may include determining that the third bin associated with the third transformed flow cytometry event data is the first bin associated with the first transformed flow cytometry event data or the second bin associated with the second transformed flow cytometry event data. The third flow cytometry event data may not be in the subsampled flow cytometry event data of the flow cytometry event data. The method may include determining a third descriptor of the third transformed flow cytometry event data based on the third bin in the first plurality of bins. The third descriptor of the third transformed flow cytometry event data associated with the third bin may be a third bin number of the third bin in the first plurality of bins. The method may include determining that the third bin, the third descriptor, and/or the third bin number are not in the memory data structure.

In some embodiments, the method includes determining that fourth flow cytometry event data associated with a fourth event in the first plurality of events is associated with a negative subsampling requirement. The generating may include generating the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the fourth flow cytometry event data associated with the fourth event. The method may include receiving a plurality of gates defining a plurality of cells of interest, wherein the fourth flow cytometry event data is associated with a cell of interest in the plurality of cells of interest. The fourth flow cytometry event data is associated with a sorted cell.

In some embodiments, the method includes converting second flow cytometry event data associated with a second event in a second plurality of events in the flow cytometry event data set within the high-dimensional space into second converted flow cytometry event data associated with the second event in the second plurality of events within the first low-dimensional space.

The second event in the second plurality of events may be associated with the positive subsampling demand. The second transformed flow cytometry event data (associated with the second event in the second plurality of events) may be associated with a second bin in the first plurality of bins. The second bin associated with the second transformed flow cytometry event data (associated with the second event in the second plurality of events) and the first bin associated with the first transformed flow cytometry event data (associated with the first event in the first plurality of events) are the same. The generating may include generating the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the second flow cytometry event data associated with the second event in the second plurality of events. The method may include determining that the last event in the first plurality of events is associated with a time parameter or a number of events exceeding a predetermined threshold. The method may include resetting the memory data structure. The method may include adding the second bin associated with the second transformed flow cytometry event data (associated with the second event in the second plurality of events) to the memory data structure. In some embodiments, the method may include receiving a subsampling parameter level. The method may comprise determining the predetermined threshold based on the sub-sampling parameter level.

In some embodiments, converting the first flow cytometry event data comprises converting the first flow cytometry event data using a first dimensionality reduction function. Converting the second flow cytometry event data may comprise converting the second flow cytometry event data using the first dimensionality reduction function. The first dimensionality reduction function and/or the second dimensionality reduction function may be a linear dimensionality reduction function. The first dimensionality reduction function and/or the second dimensionality reduction function may be a nonlinear dimensionality reduction function. The nonlinear dimensionality reduction function may be a t-distributed stochastic neighbor embedding (t-SNE). The method may comprise: first receiving the dimensionality reduction function or an identifier thereof.

In some embodiments, transforming the first flow cytometry event data comprises transforming the first flow cytometry event data into first transformed flow cytometry event data associated with the first event in a second low-dimensional space using a second dimensionality reduction function. The second low-dimensional space may be associated with a second plurality of bins.

The first transformed flow cytometry event data in the second low-dimensional space may be associated with a first bin in the second plurality of bins. Converting the second flow cytometry event data may include converting the second flow cytometry event data into second transformed flow cytometry event data associated with the second event in the second low-dimensional space using the second dimensionality reduction function. The second transformed flow cytometry event data in the second low-dimensional space may be associated with a second bin in the second plurality of bins. The first bin in the first plurality of bins may be associated with a first target cell type. The second bin in the second plurality of bins may be associated with a second target cell type. The second bin in the first plurality of bins is not associated with the first target cell type. The second bin in the first plurality of bins is not associated with the second target cell type. The first bin in the second plurality of bins is not associated with the second target cell type. The first bin in the second plurality of bins is not associated with the first target cell type. The combination of the first bin in the first plurality of bins and the first bin in the second plurality of bins is associated with the first target cell type. The combination of the second sub-bin in the first plurality of sub-bins and the second sub-bin in the second plurality of sub-bins is associated with a second target cell type. The combination of the first sub-bin in the first plurality of sub-bins and the second sub-bin in the second plurality of sub-bins is not associated with the first target cell type and the second target cell type. The combination of the second sub-bin in the first plurality of sub-bins and the first sub-bin in the second plurality of sub-bins is not associated with the first target cell type and the second target cell type.

In some embodiments, two of the first plurality of bins have the same size. Each of the first plurality of bins may have the same size. Two of the first plurality of bins may have different sizes. Two of the first plurality of bins may contain (approximately) the same amount of converted flow cytometry event data. Each of the first plurality of bins may contain approximately the same amount of converted flow cytometry event data. The method may include: determining the size of each of the first plurality of bins. The method may include: determining the size of each of the first plurality of bins based on a plurality of gates. The method may include: determining the size of each of the first plurality of bins based on the converted flow cytometry event data associated with a plurality of cells of interest.

The present invention includes embodiments of a computing system for subsampling flow cytometry event data. In some embodiments, the computing system may include: a non-transitory memory configured to store executable instructions; and a processor (e.g., a hardware processor or a virtual processor) in communication with the non-transitory memory, the processor being programmed by the executable instructions to: convert first flow cytometry event data associated with a first event in a first plurality of events in a high-dimensional space into first converted flow cytometry event data associated with the first event in a flow cytometry event data set in a first low-dimensional space, wherein the first event is associated with a positive subsampling demand, wherein the first low-dimensional space is associated with a first plurality of bins, and wherein the first converted flow cytometry event data is associated with a first bin in the first plurality of bins. The processor may be programmed by the executable instructions to convert second flow cytometry event data associated with a second event in the first plurality of events in the flow cytometry event data set in the high-dimensional space into second transformed flow cytometry event data associated with the second event in the first low-dimensional space, wherein the second event is associated with the positive subsampling demand, and wherein the second transformed flow cytometry event data is associated with a second bin in the first plurality of bins. The processor may be programmed by the executable instructions to determine that the first bin associated with the first transformed flow cytometry event data and the second bin associated with the second transformed flow cytometry event data are different. The processor may be programmed by the executable instructions to generate a subsampled flow cytometry event data set of the flow cytometry event data, the data comprising the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

In some embodiments, the processor is programmed by the executable instructions to: receive flow cytometry event data including the first flow cytometry event data and the second flow cytometry event data. The processor may be programmed by the executable instructions to: determine that the first flow cytometry event data of the first event in the first plurality of events is associated with the positive subsampling requirement. The processor may be programmed by the executable instructions to: determine that the second flow cytometry event data of the second event in the first plurality of events is associated with the positive subsampling requirement. The processor may be programmed by the executable instructions to:

Determining that the first transformed flow cytometry event data is associated with the first bin of the first plurality of bins. The processor may be programmed by the executable instructions to determine that the second transformed flow cytometry event data is associated with the second bin of the first plurality of bins.

In some embodiments, the processor is programmed by the executable instructions to determine a first descriptor of the first transformed flow cytometry event data based on the first bin in the first plurality of bins. The processor may be programmed by the executable instructions to determine a second descriptor of the second transformed flow cytometry event data based on the second bin in the first plurality of bins. The first descriptor of the first transformed flow cytometry event data associated with the first bin may be a first bin number of the first bin in the first plurality of bins; and/or the second descriptor of the second transformed flow cytometry event data associated with the second bin may be a second bin number of the first bin in the first plurality of bins. The first flow cytometry event data is associated with a first rare cell and/or the second flow cytometry event data may be associated with a second rare cell. The first rare cell and the second rare cell may be cells of different cell types.

In some embodiments, the processor is programmed by the executable instructions to: add the first bin, the first descriptor and/or the first bin number to the memory data structure; and/or add the second bin, the second descriptor and/or the second bin number to the memory data structure. In some embodiments, the processor is programmed by the executable instructions to: convert the third flow cytometry event data associated with the third event in the first plurality of events in the flow cytometry event data set in the high-dimensional space into the third transformed flow cytometry event data associated with the third event in the first low-dimensional space. The third event may be associated with the positive subsampling requirement. The third transformed flow cytometry event data may be associated with the third bin in the first plurality of bins. The processor may be programmed by the executable instructions to: determine whether the third bin associated with the third transformed flow cytometry event data is the first bin associated with the first transformed flow cytometry event data or the second bin associated with the second transformed flow cytometry event data. The third flow cytometry event data may not be in the subsampled flow cytometry event data of the flow cytometry event data. The processor may be programmed by the executable instructions to:

A third descriptor for the third transformed flow cytometry event data is determined based on the third bin in the first plurality of bins. The third descriptor for the third transformed flow cytometry event data associated with the third bin may be a third bin number for the third bin in the first plurality of bins. The processor may be programmed by the executable instructions to determine that the third bin, the third descriptor, and/or the third bin number are not in the memory data structure.

In some embodiments, the processor is programmed by the executable instructions to determine that fourth flow cytometry event data (which is associated with a fourth event in the first plurality of events) is associated with a negative subsampling requirement. To generate the subsampled flow cytometry event data set, the processor may be programmed by the executable instructions to generate the subsampled flow cytometry event data set of flow cytometry event data, the event data including the fourth flow cytometry event data associated with the fourth event. The processor may be programmed by the executable instructions to receive a plurality of gates defining a plurality of target cells. The fourth flow cytometry event data may be associated with a target cell in the plurality of target cells. The fourth flow cytometry event data is associated with a sorted cell.

In some embodiments, the processor is programmed by the executable instructions to convert second flow cytometry event data associated with a second event in the second plurality of events in the flow cytometry event data set in the high-dimensional space into second transformed flow cytometry event data associated with the second event in the second plurality of events in the first low-dimensional space. The second event in the second plurality of events may be associated with the positive subsampling requirement. The second transformed flow cytometry event data (associated with the second event in the second plurality of events) may be associated with a second bin in the first plurality of bins. The second bin associated with the second transformed flow cytometry event data (associated with the second event in the second plurality of events) and the first bin associated with the first transformed flow cytometry event data (associated with the first event in the first plurality of events) are the same. To generate the subsampled flow cytometry event data set, the processor may be programmed by the executable instructions to generate the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the second flow cytometry event data associated with the second event in the second plurality of events. The processor may be programmed by the executable instructions to:

determining that a last event in the first plurality of events is associated with a time parameter or a number of events exceeding a predetermined threshold. The processor may be programmed by the executable instructions to reset the memory data structure. The processor may be programmed by the executable instructions to add the second bin associated with the second transformed flow cytometry event data associated with the second event in the second plurality of events to the memory data structure.

In some embodiments, the processor is programmed by the executable instructions to receive a sub-sampling parameter level. The processor is programmed by the executable instructions to determine the predetermined threshold based on the sub-sampling parameter level.

In some embodiments, to convert the first flow cytometry event data, the processor may be programmed by the executable instructions to: convert the first flow cytometry event data using the first dimensionality reduction function; and/or to convert the second flow cytometry event data, the processor may be programmed by the executable instructions to: convert the second flow cytometry event data using the first dimensionality reduction function. The first dimensionality reduction function and/or the second dimensionality reduction function may be a linear dimensionality reduction function. The first dimensionality reduction function and/or the second dimensionality reduction function may be a nonlinear dimensionality reduction function. The nonlinear dimensionality reduction function may be a t-distributed stochastic neighbor embedding (t-SNE). The processor may be programmed by the executable instructions to: first receive the dimensionality reduction function or its identifier.

In some embodiments, to transform the first flow cytometry event data, the processor is programmed by the executable instructions to: transform the first flow cytometry event data into first transformed flow cytometry event data associated with the first event in a second low-dimensional space using a second dimensionality reduction function. The second low-dimensional space may be associated with a second plurality of bins. The first transformed flow cytometry event data in the second low-dimensional space may be associated with a first bin in the second plurality of bins. To transform the second flow cytometry event data, the processor is programmed by the executable instructions to: transform the second flow cytometry event data into second transformed flow cytometry event data associated with the second event in the second low-dimensional space using the second dimensionality reduction function.

The second transformed flow cytometry event data within the second low-dimensional space may be associated with a second bin in the second plurality of bins. The first bin in the first plurality of bins may be associated with a first target cell type, the second bin in the second plurality of bins may be associated with a second target cell type, the second bin in the first plurality of bins is not associated with the first target cell type, the second bin in the first plurality of bins is not associated with the second target cell type, the first bin in the second plurality of bins is not associated with the second target cell type, and/or the first bin in the second plurality of bins is not associated with the first target cell type. The combination of the first bin in the first plurality of bins and the first bin in the second plurality of bins may be associated with a first target cell type, and/or the combination of the second bin in the first plurality of bins and the second bin in the second plurality of bins may be associated with a second target cell type. A combination of the first bin in the first plurality of bins and the second bin in the second plurality of bins is not associated with the first target cell type and the second target cell type, and/or a combination of the second bin in the first plurality of bins and the first bin in the second plurality of bins is not associated with the first target cell type and the second target cell type.

In some embodiments, two of the first plurality of bins have the same size. Each of the first plurality of bins may have the same size. Two of the first plurality of bins may have different sizes. Two of the first plurality of bins may contain approximately the same amount of converted flow cytometry event data. Each of the first plurality of bins may contain approximately the same amount of converted flow cytometry event data. The processor may be programmed by the executable instructions to determine the size of each of the first plurality of bins. The processor may be programmed by the executable instructions to determine the size of each of the first plurality of bins based on a plurality of gates. The processor may be programmed by the executable instructions to determine the size of each of the first plurality of bins based on the converted flow cytometry event data associated with a plurality of cells of interest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a functional block diagram of one example of a sorting control system for analyzing and displaying biological events.

2A is a schematic diagram of a particle sorter system according to one embodiment described herein.

2B is a schematic diagram of another particle sorter system according to one embodiment described herein.

FIG3 shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization.

4 is a flow chart showing an exemplary method for subsampling flow cytometry event data.

5 is an illustrative computing system configured to implement a method of subsampling flow cytometry event data.

DETAILED DESCRIPTION

In the more detailed description below, reference is made to the accompanying drawings which form part of this document. In the accompanying drawings, similar symbols generally identify similar components unless the context indicates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be used and other changes may be made without departing from the spirit or scope of the subject matter described herein. It is readily understood that the aspects of the invention generally described herein and shown in the accompanying drawings may be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein and constitute a part of the disclosure herein.

Particle analyzers (e.g., flow cytometers and scanning cytometers) are analytical tools that characterize particles based on electro-optical measurements such as light scattering and fluorescence. In a flow cytometer, for example, particles (e.g., molecules, microbeads bound to an analyte, or individual cells) in a fluid suspension pass through a detection region where they are exposed to excitation light, typically from one or more lasers, and their light scattering and fluorescence properties are measured. Particles or their components are typically labeled with fluorescent dyes for detection. Various different particles or components can be detected simultaneously by labeling them with fluorescent dyes that have different spectral properties.

In some embodiments, the analyzer includes multiple photodetectors, one for each scattering parameter to be determined, and one or more for each different dye to be detected. For example, some embodiments include a spectral configuration in which more than one sensor or detector is used for each dye. The data obtained includes the signals measured for each of the light scattering detector and the fluorescent emission.

The particle analyzer may further include a device for recording the measured data and analyzing the data. For example, a computer connected to the detection electronic device can be used for data storage and analysis. For example, the data can be stored in a list format, where each row corresponds to the data of one particle and the column corresponds to each measured feature. Using a standard file format (e.g., a flow cytometry standard ("FCS") file format) to store data from a particle analyzer facilitates the use of a separate program and/or machine to analyze the data. When using current analysis methods, the data is typically displayed in the form of a one-dimensional histogram or a two-dimensional (2D) graph for easy visualization, but other methods can also be used to visualize multi-dimensional data.

For example, the parameters measured using flow cytometry typically include light scattered by the particles at narrow angles primarily in the forward direction (referred to as forward scatter (FSC)); light scattered by the particles in a direction orthogonal to the excitation laser (referred to as side scatter (SSC)); and light emitted by fluorescent molecules in one or more detectors (used to measure signals over a certain spectral wavelength range), or light emitted by fluorescent dyes detected primarily in the particular detector or detector array. Different cell types can be identified by light scattering characteristics and fluorescent emissions obtained/generated by labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes.

Flow cytometers and scanning cytometers are available from, for example, BD Biosciences (San Jose, CA). Flow cytometry is described in, for example, Landy et al. (eds.), Clinical Flow Cytometry, Annals of the New York Academy of Sciences, Vol. 677 (1993); Bauer et al. (eds.), Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins (1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology, No. 91, Humana Press (1997); and Shapiro, Practical Flow Cytometry, 4th Edition, Wiley-Liss (2003); the contents of these publications are incorporated herein by reference.

Fluorescence imaging microscopy is described in, for example, Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd ed., Plenum Press (1989), which is incorporated herein by reference.

The data obtained from the analysis of cells (or other particles) using multicolor flow cytometry is multidimensional, with each cell corresponding to a point in the multidimensional space defined by the measured parameters. Populations of cells or particles are identified as clusters of points in the data space. Clusters and populations can be manually identified by drawing gates around a population displayed in one or more two-dimensional graphs of the data (called "scatter plots" or "dot plots"). Alternatively, clusters can be automatically identified and gates defining the boundaries of the populations can be automatically determined. Examples of methods for automatic gating can be found in the following publications, for example, U.S. Patents No. 4,845,653; 5,627,040; 5,739,000; 5,795,727; 5,962,238; 6,014,904;

6,944,338 and 8,990,047; the contents of which are incorporated herein by reference.

Flow cytometry is an effective method for analyzing and separating biological particles (e.g., cells and constituent molecules), and therefore, it is widely used in diagnosis and treatment. The method utilizes a fluid medium to linearly separate particles so that the particles can be arranged in a row to pass through a detection device. Individual cells can be distinguished based on their location in the fluid medium and the presence or absence of detectable markers. Therefore, flow cytometry can be used to characterize and generate diagnostic profiles of biological particle populations.

Separation of biological particles has been achieved by adding a sorting or collection function to flow cytometers. Particles in the separation stream that have been detected to have one or more desired characteristics are individually separated from the sample stream by mechanical or electrical separation. This flow sorting method has been used to sort different types of cells, separate sperm containing X and Y chromosomes for animal breeding, sort chromosomes for genetic analysis, and isolate specific organisms from complex biological populations.

Gating is used to help understand and classify the large amount of data that may be generated by a sample. Given the large amount of data presented by a given sample, there is a need to effectively control the graphical display of that data.

Fluorescence activated particle sorting or cell sorting is a specialized type of flow cytometry.

Fluorescence activated particle sorting or cell sorting provides a method for sorting a heterogeneous mixture of particles into one or more containers based on the specific light scattering and fluorescence properties of each cell, sorting one cell at a time. It records the fluorescent signals from individual cells and physically separates the specific cells of interest. The abbreviation FACS is a trademark of Becton, Dickinson and Company (Franklin Lakes, NJ) and is owned by Becton Dickinson and may be used to refer to equipment that performs fluorescence activated particle sorting or cell sorting.

The particle suspension is placed near the center of a narrow, fast-flowing stream. The stream is arranged so that, on average, there are large spacings between particles relative to their diameters as they randomly arrive (e.g., a Poisson process) at the detection region. A vibration mechanism causes the outflowing fluid medium to steadily break up into single droplets containing particles previously characterized in the detection region. The system can typically be tuned so that the probability of more than one particle being present in a droplet is low. If a particle is classified as being collected, an electrical charge can be applied to the flow cell and outflow stream over a period of time to form one or more droplets that are separated from the stream. These charged droplets then pass through an electrostatic deflection system that transfers the droplets to a target container based on the charge applied to the droplets.

A sample may include thousands, if not millions, of cells. The cells may be sorted to purify the sample to cells of interest. The sorting process typically identifies three types of cells: cells of interest, non-cells of interest, and unidentifiable cells. In order to sort cells with high purity (e.g., high concentrations of cells of interest), the cell sorter that generates the droplets may electronically abort the sorting if the desired cell is too close to another undesirable cell, thereby reducing contamination of the sorted population by inadvertent inclusion of undesirable particles in droplets containing particles of interest.

The present invention discloses a system, apparatus, computer-readable medium and method for subsampling flow cytometry event data. In some embodiments, the method comprises: under the control of a processor: converting first flow cytometry event data associated with a first event in a first plurality of events in a high-dimensional space into first converted flow cytometry event data associated with the first event in a first low-dimensional space. The first event may be associated with a positive subsampling demand.

The first low-dimensional space may be associated with a first plurality of bins. The first transformed flow cytometry event data may be associated with the first bin of the first plurality of bins. The method may include converting second flow cytometry event data associated with a second event of the first plurality of events in the high-dimensional space into second transformed flow cytometry event data associated with the second event in the first low-dimensional space. The second event may be associated with the positive subsampling requirement. The second transformed flow cytometry event data may be associated with a second bin of the first plurality of bins. The method may include determining that the first bin associated with the first transformed flow cytometry event data and the second bin associated with the second transformed flow cytometry event data are different. The method may include generating the subsampled flow cytometry event data set of the flow cytometry event data, the data including the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

The present invention includes embodiments of a computing system for subsampling flow cytometry event data. In some embodiments, the computing system may include: a non-transitory memory configured to store executable instructions; and a processor (e.g., a hardware processor or a virtual processor) in communication with the non-transitory memory, the processor being programmed by the executable instructions to: convert first flow cytometry event data associated with a first event in a first plurality of events in a high-dimensional space into first converted flow cytometry event data associated with the first event in a flow cytometry event data set in a first low-dimensional space, wherein the first event is associated with a positive subsampling demand, wherein the first low-dimensional space is associated with a first plurality of bins, and wherein the first converted flow cytometry event data is associated with a first bin in the first plurality of bins. The processor may be programmed by the executable instructions to convert second flow cytometry event data associated with a second event of the first plurality of events in the flow cytometry event data set in the high-dimensional space into second transformed flow cytometry event data associated with the second event in the first low-dimensional space, wherein the second event is associated with the positive subsampling requirement, and wherein the second transformed flow cytometry event data is associated with a second bin of the first plurality of bins.

The processor may be programmed by the executable instructions to determine that the first bin associated with the first transformed flow cytometry event data and the second bin associated with the second transformed flow cytometry event data are different. The processor may be programmed by the executable instructions to generate the subsampled flow cytometry event data set of the flow cytometry event data, the data comprising the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

definition

The terms used herein and specifically set forth below have the following definitions. Unless otherwise defined in this section, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

As used herein, "system," "apparatus," "device," and "equipment" generally encompass hardware (e.g., mechanical and electronic hardware) and, in some embodiments, related software (e.g., a dedicated computer program for graphics control) components.

As used herein, "event" or "event data" generally refers to data (e.g., a combined data packet) measured from a single particle (e.g., a cell or a synthetic particle). Typically, the data measured from a single particle includes a number of parameters or features, including one or more light scattering parameters or features, and at least one other parameter or feature obtained using fluorescence detected from the particle, for example, the fluorescence intensity. Therefore, each event can be represented as a vector of parameter and feature measurements, where each measured parameter or feature corresponds to a dimension of the data space. In some embodiments, the data measured from a single particle includes images, electrical data, time data, or acoustic data. An event can be associated with an experiment, assay, or sample source (which can be identified by the measurement data).

As used herein, a "population" or "subpopulation" of particles (e.g., cells or other particles) generally refers to a group of particles having properties (e.g., optical, impedance, or time properties) with respect to one or more measured parameters that cause the measured parameter data to form clusters in the data space. Thus, a population can be identified as a cluster in the data. Conversely, each data cluster is typically interpreted as corresponding to a particular type of cell or particle population, although clusters corresponding to noise or background are also typically observed.

Clusters may be defined in a subset of the dimensions (eg, relative to a subset of measured parameters), corresponding to groups that differ only in a subset of measured parameters or features (extracted from the cell or particle measurements).

As used herein, "gate" generally refers to the boundaries of a classifier that identifies a subset of data of interest. In cytometry, a gate may be associated with a specific set of events of interest. As used herein, "gating" generally refers to the process of classifying a given data set using a defined gate, where the gate can be one or more regions of interest combined with Boolean logic.

Specific examples of various embodiments and systems in which the embodiments and systems may be implemented are described further below.

Sorting control system

1 shows a functional block diagram of one example of a sorting control system (eg, analysis controller 100) for analyzing and displaying biological events. Analysis controller 100 can be configured to implement various processes for controlling the graphical display of biological events.

The particle analyzer or sorting system 102 can be configured to collect biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 102 can be configured to provide the biological event data to the analysis controller 100. A data communication channel can be included between the particle analyzer 102 and the analysis controller 100. The biological event data can be provided to the analysis controller 100 via the data communication channel.

Analysis controller 100 may be configured to receive biological event data from particle analyzer 102. The biological event data received from particle analyzer 102 may include flow cytometry event data. Analysis controller 100 may be configured to provide a graphical display including a first graph of biological event data to display device 106. For example, analysis controller 100 may be further configured to render a region of interest as a gate around a plurality of biological event data shown by display device 106, which is overlaid on the first graph. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest drawn on a single parameter histogram or a bivariate graph.

Alternatively, analysis controller 100 may be further configured to display the bio-event data on display device 106 inside the door in a manner different from other events in the bio-event data outside the door. For example, analysis controller 100 may be configured to make the color of the bio-event data contained inside the door different from the color of the bio-event data outside the door. Display device 106 may be implemented in the form of a display, tablet computer, smart phone, or other electronic device configured to display a graphical interface.

The analysis controller 100 may be configured to receive a door selection signal identifying the door from a first input device. For example, the first input device may be implemented in the form of a mouse 110. The mouse 110 may send a door selection signal to the analysis controller 100 to determine the door to be displayed on the display device 106 or manipulated via the display device 106 (e.g., when the cursor is located there, click on or in the desired door). In some embodiments, the first device may be implemented in the form of a keyboard 108 or other device (e.g., a touch screen, a stylus, an optical detector, or a voice recognition system) for providing an input signal to the analysis controller 100. Some input devices may include multiple input functions. In such embodiments, the input functions may be individually considered to be an input device. For example, as shown in Figure 1, the mouse 110 may include a right mouse button and a left mouse button, both of which may generate a trigger event.

The trigger event may cause the analysis controller 100 to change the manner in which the data is displayed (actually displaying a portion of the data on the display device 106), and/or provide input for further processing, such as selecting a target population for particle sorting.

In some embodiments, the analysis controller 100 can be configured to detect when the mouse 110 initiates a gate selection. The analysis controller 100 can be further configured to automatically modify the graph visualization to facilitate the gating process. The modification can be based on a particular distribution of the bio-event data received by the analysis controller 100.

Analysis controller 100 may be coupled to storage device 104. Storage device 104 may be configured to receive and store biological event data from analysis controller 100. Storage device 104 may also be configured to receive and store flow cytometry event data from analysis controller 100. Storage device 104 may also be configured to allow analysis controller 100 to retrieve biological event data, such as flow cytometry event data.

The display device 106 can be configured to receive display data from the analysis controller 100. The display data can include graphs of biological event data and gates outlining portions of the graphs. The display device 106 can be further configured to change the information displayed based on input received from the analysis controller 100 and input received from the particle analyzer 102, the storage device 104, the keyboard 108, and/or the mouse 110.

In some embodiments, the analysis controller 100 can generate a user interface to receive sample events for sorting. For example, the user interface can include controls for receiving sample events or sample images. Sample events or images or sample gates can be provided before event data for the sample is collected or based on an initial event set for a portion of the sample.

Particle Sorting System

A common flow sorting technique (referred to as "electrostatic cell sorting") employs droplet sorting, in which a stream or moving column of liquid containing linearly separated particles is broken into droplets, and droplets containing the particles of interest are charged and deflected into a collection tube by an electric field. Droplet sorting systems are capable of forming droplets at a rate of 100,000 drops per second in a fluid medium passing through a nozzle having a diameter of less than 100 microns. Droplet sorting typically requires that the droplets detach from the stream at a certain distance from the nozzle head. The distance is typically about a few millimeters from the nozzle head, and for an undisturbed fluid medium, the distance is stable and can be maintained by oscillating the nozzle head at a predetermined frequency and an amplitude that keeps the detachment constant. For example, in some embodiments, the amplitude of a sinusoidal waveform voltage pulse is adjusted at a given frequency to keep the detachment stable and constant.

Typically, linearly separated particles in the stream are characterized by their passage through an observation point located within a flow cell or cuvette or directly below a nozzle tip. Once a particle is determined to meet one or more desired criteria, it is possible to predict when it will reach the droplet breakup point and separate from the stream as a droplet. Ideally, a brief charge is applied to the fluid medium before the droplet containing the selected particle separates from the stream, and then the droplet is grounded immediately after breakup.

The droplets to be sorted will remain charged when they are separated from the fluid medium, while all other droplets are uncharged. The charged droplets are laterally deviated from the downward trajectory of other droplets under the action of the electric field and are collected in the sample tube. The uncharged droplets fall directly into the discharge tube.

FIG2B is a schematic diagram of a particle sorter system 200 (e.g., particle analyzer 102) according to one embodiment described herein. In some embodiments, the particle sorter system 200 is a cell sorter system. As shown in FIG2A, a droplet formation sensor 202 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 201 (which may be coupled to, may include, or may be a nozzle 203). Within the fluid conduit 201, a sheath fluid 204 fluidically aggregates a sample fluid 206 (containing particles 209) into a moving liquid column 208 (e.g., a stream). Within the moving liquid column 208, particles 209 (e.g., cells) are aligned to pass through a monitored area 211 (e.g., a laser stream intersection) irradiated by an irradiation source 212 (e.g., a laser). The droplet formation sensor 202 vibrates to cause the moving liquid column 208 to break into a plurality of droplets 210, some of which contain particles 209.

In operation, a detection station 214 (e.g., an event detector) determines when a particle of interest (or cell of interest) passes through the monitored area 211. The detection station 214 feeds a timing circuit 228, which in turn feeds a transient charging circuit 230. At the droplet breakup point, after notification of a timing droplet delay (Δt), a transient charge can be applied to the moving liquid column 208 to charge the droplets of interest. The droplets of interest can include one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplets into a container such as a collection tube or a porous or microporous sample plate, where the holes or micropores can be associated with specific droplets of interest. As shown in FIG. 2A , the droplets can be collected in a discharge container 238.

A detection system 216 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as the particle of interest passes through the monitored area 211. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 216 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 216 can be fed with an amplitude signal 220 and/or a phase 218 signal, which in turn is fed (via amplifier 222) to an amplitude control circuit 226 and/or a frequency control circuit 224.

The amplitude control circuit 226 and/or the frequency control circuit 224 in turn controls the drop formation sensor 202. The amplitude control circuit 226 and/or the frequency control circuit 224 may be included in the control system.

In some embodiments, the sorting electronics (e.g., detection system 216, detection station 214, processor 240) may be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decision may be included in the event data of the particle. In some embodiments, the detection system 216 and the detection station 214 may be implemented in the form of a single detection unit or coupled in a communication manner so that event measurements may be collected by one of the detection system 216 or the detection station 214 and provided to the non-collecting element.

FIG. 2B is a schematic diagram of a particle sorter system according to an embodiment described herein. The particle sorter system 200 shown in FIG. 2B includes deflection plates 252 and 254. The charge is applied by the stream charge wire in the barb. This produces a droplet stream 210 containing particles 210 for analysis. The particles can be irradiated with one or more light sources (e.g., lasers) to produce light scattering and generate fluorescence information. The particle information is analyzed by, for example, a sorting electronic device or other detection system (not shown in FIG. 2B). Deflection plates 252 and 254 can be independently controlled to attract or repel the charged droplets to guide the droplets to a destination collection container (e.g., one of 272, 274, 276, or 278). As shown in FIG. 2B, deflection plates 252 and 254 can be controlled to guide particles along a first path 262 toward a container 274 or along a second path 268 toward a container 278. If the particle is not a particle of interest (e.g., within a specified sorting range, does not show scattering or illumination information), the deflector plate may cause the particle to continue flowing along the flow path 264. Such uncharged droplets may enter a waste container via, for example, an aspirator 270.

The sorting electronics may be included to begin collecting measurements, receive the fluorescence signal of a particle, and determine how to adjust the deflection plates to sort the particle. An exemplary implementation of the embodiment shown in FIG2B includes a BD FACSAria ^™ series flow cytometer commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

In some embodiments, one or more of the components described herein applicable to the particle sorter system 200 can be used for particle analysis and characterization, whether or not the particles are physically sorted into a collection container. Similarly, one or more of the components described herein applicable to the particle analysis system 300 (FIG. 3) can also be used for particle analysis and characterization, whether or not the particles are physically sorted into a collection container. For example, one or more of the components described herein in the particle sorter system 200 or the particle analysis system 300 can be used to group particles or display them in a tree, the tree including at least three of the groupings described herein.

FIG3 shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 300 is a flow system. For example, the particle analysis system 300 shown in FIG3 can be configured to perform all or part of the methods described herein. The particle analysis system 300 includes a fluidics system 302. The fluidics system 302 can include or be coupled to a sample tube 310 and a moving liquid column within the sample tube, in which particles 330 (e.g., cells) in the sample move along a common sample path 320.

The particle analysis system 300 includes a detection system 304 configured to collect signals from each particle as each particle passes through one or more detection stations along the common sample path. Detection station 308 generally refers to a monitored area 340 of the common sample path. In some embodiments, detection can include detecting light or one or more other characteristics of the particle as the particle 330 passes through the monitored area 340. In FIG. 3 , a detection station 308 is shown with a monitored area 340. Some embodiments of the particle analysis system 300 can include multiple detection stations. In addition, some detection stations may monitor more than one area.

Each signal is assigned a signal value to form a data point for each particle. As described above, the data may be referred to as event data. The data point may be a multi-dimensional data point that includes values for each measured characteristic of the particle. The detection system 304 is configured to collect a series of the data points within a first time interval.

The particle analysis system 300 also includes a control system 306. The control system 306 may include one or more processors, an amplitude control circuit 226, and/or a frequency control circuit 224, as shown in FIG2B. The control system 206 may be operatively associated with the fluidics system 302.

The control system 206 can be configured to generate a calculated signal frequency for at least a portion of the first time interval based on a Poisson distribution and a number of data points collected by the detection system 304 during the first time interval. The control system 306 can be further configured to generate an experimental signal frequency based on the number of data points during the portion of the first time interval. In addition, the control system 306 can compare the experimental signal frequency to the calculated signal frequency or the predetermined signal frequency.

Subsampling flow cytometry event data

The present invention includes systems, devices, computer-readable media, and methods for subsampling a data set (e.g., a large, high-dimensional data set) that can weight rare events and populations so that the rare events and populations are appropriately shown in the resulting subset. In some embodiments, where it is not desirable to save the entire data set, a subset of the data set that retains all populations (e.g., all populations including rare cells and populations) can be shown in a subset of the data set or a subsampled data set. In some embodiments, the subset or subsampled data set retains representative samples from rare subpopulations. In some embodiments, the data set can be subsampled without discarding rare events or target events (e.g., corresponding to rare cells or target cells). The system automatically detects rare events and saves them, while more actively discarding common events.

In some embodiments, the data may be subsampled non-randomly (e.g., semi-randomly). A desired subsampling rate may be selected and the data may then be fed sequentially through the subsampling method. The method may decide to save or discard events (or multi-dimensional event data associated with an event) on a per-event basis. The ability to discard events without analyzing the overall distribution of events eliminates the need to save and analyze large amounts of data.

The user can select the subsampling parameter level. The subsampling parameter level can determine the duration of the algorithm's "memory".

The user can select one or more transformation or "fingerprinting" functions. The transformation or fingerprinting function can be a mathematical equation that transforms the data from a high-dimensional space data to a low-dimensional space data in some way, such as. For example, the transformation or fingerprinting function can be a t-distributed stochastic neighbor embedding (t-SNE). Events can be transformed into low-dimensional space events, and the space is divided into bins. The bin numbers can serve as descriptors for the events.

In some embodiments, binning can be consistent or based on event density. Binning can be based on automatic population detection. Binning can be based in part on arbitrarily drawn gates (e.g., user-drawn gates). In some embodiments, a transformation or fingerprinting function can transform events so that similar events have the same identifier. The identifier can be smaller than the data used to generate the identifier. The transformation calculation may be computationally cheaper. The reversal of the transformation or function may or may not exist. In some embodiments, multiple fingerprinting functions can be used. For example, different target groups can be defined using different fingerprinting functions. As another example, a target group can be defined based on the combined output of multiple fingerprinting functions.

Third, the user can describe events that should not be subsampled. For example, a gate around the region of interest can be drawn automatically or by the user. Events within the gate around the region of interest may not be subsampled. As another example, any sorted events (e.g., unsorted cells) may not be subsampled.

The event data may be subsampled using the subsampling method disclosed herein. For example, for each event:

ii. Check if the event should be subsampled. If the answer is no, save the event.

iii. If the event should be sub-sampled, a descriptor is generated using the fingerprinting function.

1. Compare the descriptor to the algorithm "memory". Have you seen this descriptor before?

a. Seen. Abandoned event

b. Not seen before. Save the event and keep the descriptor in memory.

iv. Check time or number of events. If the time and/or number of events exceeds the corresponding thresholds generated based on the user's subsampling parameter level, reset the algorithm memory.

The subsampling method disclosed herein (a non-random subsampling method) can be a complementary or supplementary random subsampling method for subsampling large data sets. When randomly sampling data, rare populations can be eliminated. The subsampling method can include some, most or all rare populations. For particle analysis (e.g., flow cytometric analysis), rare events may be extremely valuable. It may be useful to retain rare populations in order to detect rare populations when analyzing a reduced data set. The non-random subsampling method can intentionally deviate from the random sampling process to make rare populations more likely to be shown in the final subsampled data set.

A simple partitioning of the data space without dimensionality reduction may result in bins with sparse data due to the so-called "curse of dimensionality". The dimensionality reduction transformation or function employed may be a relation-preserving embedding that allows binning in a lower dimensional space and allows for more efficient grouping of the data prior to subsampling.

Secondary sampling method for particle analysis event data

FIG. 4 is a flow chart showing an exemplary method 400 for subsampling particle sorting event data (e.g., flow cytometry event data). Method 400 may be embodied in a set of executable program instructions stored on a computer-readable medium (e.g., one or more disk drives of a computing system). For example, a computing system 500 shown in FIG. 5 and described in more detail below may execute a set of executable program instructions to implement method 400. When method 400 is enabled, the executable program instructions may be loaded into a memory such as a RAM and executed by one or more processors of the computing system 500. Although method 400 is described with respect to the computing system 500 shown in FIG. 5, the description is provided for illustrative purposes only and is not intended to be limited. In some embodiments, method 400 or portions thereof may be executed serially or in parallel by a plurality of computing systems.

After method 400 starts at box 404, method 400 proceeds to box 408, where the computing system converts the first flow cytometry event data associated with the first event in the first plurality of events in the flow cytometry event data set in the high-dimensional space into the first converted flow cytometry event data associated with the first event in the first low-dimensional space. The first event may be associated with a positive subsampling requirement. For example, when the flow cytometry event data containing the first flow cytometry event data is subsampled, the subsampled flow cytometry event data may not include the first flow cytometry event data. The first low-dimensional space may be associated with a first plurality of bins. The first converted flow cytometry event data may be associated with the first bin in the first plurality of bins. When generating the subsampled flow cytometry event data, the computing system may indicate (e.g., in a data structure) that the first flow cytometry event data should be included.

In some embodiments, the computing system may receive flow cytometry event data including the first flow cytometry event data. The computing system may determine that the first flow cytometry event data for the first event in the first plurality of events is associated with the positive subsampling requirement. The computing system may determine that the first transformed flow cytometry event data is associated with the first bin in the first plurality of bins.

The processor may be programmed by the executable instructions to: determine a first descriptor of the first transformed flow cytometry event data based on the first bin in the first plurality of bins. The first descriptor of the first transformed flow cytometry event data associated with the first bin may be a first bin number of the first bin in the first plurality of bins. The computing system may: add the first bin, the first descriptor, and/or the first bin number to a memory data structure.

In some embodiments, two of the first plurality of bins have the same size. Each of the first plurality of bins may have the same size. Two of the first plurality of bins may have different sizes. Two of the first plurality of bins may contain approximately the same amount of converted flow cytometry event data. Each of the first plurality of bins may contain approximately the same amount of converted flow cytometry event data. The computing system may: determine the size of each of the first plurality of bins. The processor may be programmed by the executable instructions to: determine the size of each of the first plurality of bins based on a plurality of gates. The computing system may: determine the size of each of the first plurality of bins based on the converted flow cytometry event data associated with a plurality of cells of interest.

In some embodiments, to convert the first flow cytometry event data, the computing system may: convert the first flow cytometry event data using a first dimensionality reduction function. The first dimensionality reduction function may be a linear dimensionality reduction function. The first dimensionality reduction function may be a nonlinear dimensionality reduction function. The nonlinear dimensionality reduction function may be a t-distributed stochastic neighbor embedding (t-SNE). The computing system may: first receive the dimensionality reduction function or an identifier thereof.

The method 400 proceeds to block 412, wherein the computing system may convert second flow cytometry event data associated with a second event in the first plurality of events in the flow cytometry event data set in the high-dimensional space into second converted flow cytometry event data associated with the second event in the first low-dimensional space. The second event may be associated with the positive subsampling requirement. The second converted flow cytometry event data may be associated with a second bin in the first plurality of bins. In some embodiments, to convert the second flow cytometry event data, the computing system may: convert the first flow cytometry event data using a second dimensionality reduction function. The first dimensionality reduction function and the second dimensionality reduction function may be the same.

In some embodiments, the computing system may receive flow cytometry event data including the second flow cytometry event data. The computing system may determine that the second flow cytometry event data for the second event in the first plurality of events is associated with the positive subsampling requirement. The computing system may determine that the second transformed flow cytometry event data is associated with the second bin in the first plurality of bins.

The processor may be programmed by the executable instructions to determine a second descriptor of the second transformed flow cytometry event data based on the second bin in the first plurality of bins. The second descriptor of the second transformed flow cytometry event data associated with the second bin may be a second bin number of the first bin in the first plurality of bins. The computing system may add the second bin, the second descriptor, and/or the second bin number to the memory data structure.

The first flow cytometry event data is associated with a first rare cell and/or the second flow cytometry event data may be associated with a second rare cell.The first rare cell and the second rare cell may be cells of different cell types.

From block 412, method 400 proceeds to block 416, where the computing system may determine that the first bin associated with the first transformed flow cytometry event data and the second bin associated with the second transformed flow cytometry event data are different. When generating the subsampled flow cytometry event data, the computing system may indicate (e.g., in a data structure) that the second flow cytometry event data should be included.

In some embodiments, to transform the first flow cytometry event data, the computing system may: transform the first flow cytometry event data into first transformed flow cytometry event data associated with the first event in a second low-dimensional space using a second dimensionality reduction function. The second low-dimensional space may be associated with a second plurality of bins. The first transformed flow cytometry event data in the second low-dimensional space may be associated with a first bin in the second plurality of bins. To transform the second flow cytometry event data, the computing system may: transform the second flow cytometry event data into second transformed flow cytometry event data associated with the second event in the second low-dimensional space using the second dimensionality reduction function. The second transformed flow cytometry event data in the second low-dimensional space may be associated with a second bin in the second plurality of bins. The first sub-bin in the first plurality of sub-bins may be associated with a first target cell type, the second sub-bin in the second plurality of sub-bins may be associated with a second target cell type, the second sub-bin in the first plurality of sub-bins is not associated with the first target cell type, the second sub-bin in the first plurality of sub-bins is not associated with the second target cell type, the first sub-bin in the second plurality of sub-bins is not associated with the second target cell type, and/or the first sub-bin in the second plurality of sub-bins is not associated with the first target cell type.

A first combination of the first bin in the first plurality of bins and the first bin in the second plurality of bins may be associated with a first cell type of interest, and/or a second combination of the second bin in the first plurality of bins and the second bin in the second plurality of bins may be associated with a second cell type of interest. A first combination of the first bin in the first plurality of bins and the second bin in the second plurality of bins is not associated with the first cell type of interest and the second cell type of interest, and/or a second combination of the second bin in the first plurality of bins and the first bin in the second plurality of bins is not associated with the first cell type of interest and the second cell type of interest. The computing system may determine that the first combination and the second combination are different.

Method 400 proceeds to block 420, where the computing system may generate subsampled flow cytometry event data of the flow cytometry event data, the data comprising the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

In some embodiments, the computing system may: convert third flow cytometry event data associated with a third event in the first plurality of events in the flow cytometry event data set in the high dimensional space into third transformed flow cytometry event data associated with the third event in the first low dimensional space. The third event may be associated with the positive subsampling requirement. The third transformed flow cytometry event data may be associated with the third bin in the first plurality of bins. The processor may be programmed by the executable instructions to: determine whether the third bin associated with the third transformed flow cytometry event data is the first bin associated with the first transformed flow cytometry event data or the second bin associated with the second transformed flow cytometry event data. The third flow cytometry event data may not be in the subsampled flow cytometry event data of the flow cytometry event data. The computing system may: determine a third descriptor of the third transformed flow cytometry event data based on the third bin in the first plurality of bins. The third descriptor of the third transformed flow cytometry event data associated with the third bin may be a third bin number of the third bin in the first plurality of bins. The computing system may determine that the third bin, the third descriptor, and/or the third bin number are not in the memory data structure.

In some embodiments, the computing system includes: determining that fourth flow cytometry event data (which is associated with a fourth event in the first plurality of events) is associated with a negative subsampling requirement. The computing system may: generate the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the fourth flow cytometry event data associated with the fourth event. The computing system may: receive a plurality of gates defining a plurality of cells of interest. The fourth flow cytometry event data may be associated with a cell of interest in the plurality of cells of interest. The fourth flow cytometry event data is associated with a sorted cell.

In some embodiments, the computing system may: transform second flow cytometry event data associated with a second event in a second plurality of events in the flow cytometry event data set in the high dimensional space into second transformed flow cytometry event data associated with the second event in the second plurality of events in the first low dimensional space. The second event in the second plurality of events may be associated with the positive subsampling demand.

The second transformed flow cytometry event data (associated with the second event in the second plurality of events) may be associated with a second bin in the first plurality of bins. The second bin associated with the second transformed flow cytometry event data (associated with the second event in the second plurality of events) and the first bin associated with the first transformed flow cytometry event data (associated with the first event in the first plurality of events) are the same. The computing system may: generate the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the second flow cytometry event data associated with the second event in the second plurality of events.

The computing system may determine that a last event in the first plurality of events is associated with a time parameter or a number of events that exceeds a predetermined threshold. The computing system may reset the memory data structure. The processor may be programmed by the executable instructions to add the second bin associated with the second transformed flow cytometry event data associated with the second event in the second plurality of events to the memory data structure. In some embodiments, the computing system may receive a subsampling parameter level. The computing system may determine the predetermined threshold based on the subsampling parameter level.

The method 400 ends at block 424 .

Execution Environment

Fig. 5 depicts the overall architecture of an example computing device 500, which is configured to operate metabolites, annotations and gene integration systems disclosed herein. The overall architecture of the computing device 500 shown in Fig. 5 includes the arrangement of computer hardware and software components. The computing device 500 may include more (or less) elements than the elements shown in Fig. 5. However, when providing an implementable disclosure, it is not necessary to show all such conventional elements. As shown in the figure, the computing device 500 includes a processing unit 510, a network interface 520, a computer-readable medium driver 530, an input/output device interface 540, a display 550 and an input device 560, all of which can communicate with another device via a communication bus. The network interface 520 can be connected to one or more networks or computing systems. Therefore, the processing unit 510 can receive information and instructions from other computing systems or services via a network.

The processing unit 510 may also communicate with the memory 570 and may further provide output information to the optional display 550 via the input/output device interface 540. The input/output device interface 540 may also accept input from an optional input device 560, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope or other input device.

Memory 570 may contain computer program instructions (grouped into modules or components in some embodiments) that are executed by processing unit 510 to implement one or more embodiments. Memory 570 typically includes RAM, ROM, and/or other persistent, auxiliary, or non-transitory computer-readable media. Memory 570 may store an operating system 572 that provides computer program instructions for use by processing unit 510 in general management and operation of computing device 500. Memory 570 may further include computer program instructions and other information for implementing aspects of the present invention.

For example, in one embodiment, the memory 570 includes a subsampling module 574 for subsampling the particle analysis event data, such as the subsampling method 400 described in Figure 4. In addition, the memory 570 may include or communicate with a data storage area 590 and/or one or more other data storage areas storing the generated flow cytometry event data set or the subsampled flow cytometry event data set.

the term

As used herein, the term "determining" encompasses a variety of activities. For example, "determining" may include calculating, computer computing, processing, deriving, investigating, looking up (e.g., looking up in a table, database, or another data structure), ascertaining, etc. In addition, "determining" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), etc. In addition, "determining" may include resolving, selecting, choosing, establishing, etc.

The term "providing" as used herein encompasses a variety of activities. For example, "providing" may include storing a value at a location on a storage device that facilitates subsequent retrieval, sending a value directly to a recipient via at least one wired or wireless communication medium, sending or storing a reference to a value, etc.

"Providing" may also include encoding, decoding, encryption, decryption, confirmation, verification, etc. through hardware.

The terms "selectively" or "selective" as used herein may encompass a variety of activities. For example, a "selective" process may include determining an option from a plurality of options. A "selective" process may include one or more of the following: dynamically determined inputs, preconfigured inputs, or user-initiated inputs for determination. In some embodiments, n-input switching may be included to provide selective functionality, where n is the number of inputs used to make a selection.

The term "message" as used herein encompasses various formats for conveying (e.g., sending or receiving) information. A message may include a machine-readable aggregation of information, such as an XML document, a fixed field message, a comma-delimited message, etc. In some embodiments, a message may include a signal for transmitting one or more information representations. Although described in singular form, it should be understood that a message may be composed, sent, stored, received, etc., in multiple parts.

As used herein, "user interface" (also referred to as interactive user interface, graphical user interface or UI) may refer to a web-based user interface including data fields, buttons or other interactive controls for receiving input signals or providing electronic information or providing information to a user in response to any received input signals. The user interface may be implemented in a web-based user interface such as Hypertext Markup Language (HTML), JAVASCRIPT ^™ , FLASH ^™ , JAVA ^™ , .NET ^™ , WINDOWSOS ^™ ,

The UI may be implemented in whole or in part using technologies such as macOS ^™ , Web services, and Rich Site Summary (RSS). In some embodiments, the UI may be included in a standalone client (e.g., a thick client, a fat client) configured to communicate (e.g., send or receive data) according to one or more aspects described.

As used herein, a "data storage area" may be embodied in a hard disk drive, solid-state memory, and/or any other type of non-transitory computer-readable storage medium that can access a device or be accessed by the device, including an access device, a server, or other computing device, etc. As is known in the art, the data storage area may also or alternatively be distributed or partitioned across multiple local and/or remote storage devices without departing from the scope of the present invention. In other embodiments, the data storage area may include or be embodied in a data storage network service.

Those skilled in the art will appreciate that any of a variety of different technologies and techniques may be used to represent information, messages, and signals. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above specification may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or optical particles, or any combination thereof.

It will be further understood by those skilled in the art that the various illustrative logic blocks, modules, circuits and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented in the form of electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been generally described above with respect to their functions. Whether this function is implemented in the form of hardware or software depends on the specific application and the design constraints for the entire system. The technician can perform the functions in various ways for each specific application, but this execution decision should not be interpreted as causing departure from the scope of the present invention.

The technology described herein can be implemented in the form of hardware, software, firmware or any combination thereof. The technology can be implemented in any of a variety of devices, for example, a specially programmed event processing computer, a wireless communication device or an integrated circuit device. Any function described as a module or component can be executed together in an integrated logic device, or separately in a discrete but interoperable logic device. If implemented in software form, the technology can be implemented at least in part by a computer-readable data storage medium, which includes instructions, and when the instructions are executed, one or more of the above methods are executed. The computer-readable data storage medium may constitute a part of a computer program product, which may include packaging materials. The computer-readable medium may include a memory or a data storage medium, for example, a random access memory (RAM) (e.g., a synchronous dynamic random access memory (SDRAM)), a read-only memory (ROM), a non-volatile random access memory (NVRAM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic or optical data storage medium, etc. The computer-readable medium may be a non-temporary storage medium.

Alternatively or additionally, the techniques may be implemented at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computing device, such as a propagated signal or wave.

The program code can be executed by a specially programmed sorting strategy processor, which can include one or more processors, for example, one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuit systems. The graphics processor can be specially configured to perform any of the techniques described in the present invention. A combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration in at least part of the data connection) can perform one or more of the functions described. In some aspects, the functions described herein can be provided in a dedicated software module or hardware module configured for encoding and decoding, or incorporated into a dedicated sorting control card.

Those skilled in the art will understand that, in general, the terms used herein, particularly in the appended claims (e.g., in the body of the appended claims), should generally be understood as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to", the term "having" should be interpreted as "having at least", etc.). Those skilled in the art will also understand that if a specific number is intended to be indicated in an introduced claim, such intention will be explicitly indicated in the claim, and in the absence of such explicit indication, such intention will be deemed not to exist. For example, to aid understanding, the appended claims may use the introductory phrases "at least one" and "one or more" to introduce features in the claims. However, the use of such phrases should not be interpreted as implying that a claim feature introduced by the indefinite article "a" or "an" limits any particular claim containing that feature to embodiments containing only one of that feature, even if the claim includes both the introductory phrase "one or more" or "at least one" and an indefinite article such as "a" or "an" (for example, "a" and/or "an" should be interpreted as meaning "at least one" or "one or more"); the same is true when a definite article is used to introduce a feature in a claim.

In addition, even if a specific number of claimed features is explicitly indicated, those skilled in the art will recognize that such enumeration should be interpreted as meaning at least the number listed (e.g., the phrase "two features" without other modifiers means at least two of the features, or two or more of the features). In addition, where expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (e.g., "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and/or A, B, and C, etc.). Where expressions such as "at least one of A, B, or C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (e.g., "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and/or A, B, and C, etc.). Those skilled in the art should also understand that any transitional conjunction and/or phrase that represents two or more optional items, whether in the specification, claims or drawings, should be understood to include the possibility of one of these items, either of these items or both items. For example, the phrase "A or B" should be understood to include the possibility of "A" or "B" or "A and B".

In addition, where features or aspects of the invention are described in terms of Markush groups, those skilled in the art will recognize that the invention is also thereby described in terms of any individual member or subgroup of members of the Markush group.

It will be appreciated by those skilled in the art that, for any and all purposes, such as to provide a written description, all ranges disclosed herein also include any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily viewed as fully describing and achieving at least bisection, trisection, quartering, quintillation, decimalization, etc. of the range. As a non-limiting example, each range discussed herein can be easily divided into a lower third, a middle third, and an upper third, etc. It will be appreciated by those skilled in the art that all such as "until", "at least", "greater than", "less than" and the like language include the listed numbers, and refer to the range that can be subsequently divided into sub-ranges as described above. Finally, it will be appreciated by those skilled in the art that the range includes each individual number. Therefore, for example, a group having 1-3 units refers to a group having 1, 2 or 3 units. Similarly, a group having 1-5 units refers to a group having 1, 2, 3, 4 or 5 units, and so on.

The method disclosed herein includes one or more steps or actions for implementing the method. The method steps and/or actions may be interchangeable with each other without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims.

Although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are provided for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims (74)

1. A method for subsampling flow cytometry event data, comprising: under control of the processor:

Converting first flow cytometry event data in a high-dimensional spatial flow cytometry event dataset associated with a first event of a first plurality of events into first converted flow cytometry event data in a first low-dimensional space associated with the first event, wherein the first event is associated with a positive subsampling requirement, wherein the first low-dimensional space is associated with a first plurality of bins, and wherein the first converted flow cytometry event data is associated with a first bin of the first plurality of bins;

Converting second flow cytometry event data in the set of flow cytometry event data associated with a second event of the first plurality of events within the high-dimensional space to second converted flow cytometry event data associated with the second event within the first low-dimensional space, wherein the second event is associated with the positive sub-sampling requirement, and wherein the second converted flow cytometry event data is associated with a second bin of the first plurality of bins;

Determining that the first bin associated with the first converted flow cytometry event data and the second bin associated with the second converted flow cytometry event data are different; and

A subsampled flow cytometry event data set of the flow cytometry event data is generated, the data comprising the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

2. The method as claimed in claim 1, comprising: flow cytometry event data comprising the first flow cytometry event data and the second flow cytometry event data is received.

3. The method according to any one of claims 1-2, comprising:

determining that the first flow cytometry event data of the first event of the first plurality of events is associated with the positive sub-sampling requirement; and

Determining that the second flow cytometry event data for the second event of the first plurality of events is associated with the positive sub-sampling requirement.

4. The method according to any one of claims 1-2, comprising:

determining that the first converted flow cytometry event data is associated with the first bin of the first plurality of bins; and

Determining that the second converted flow cytometry event data is associated with the second bin of the first plurality of bins.

5. The method according to any one of claims 1-2, comprising:

Determining a first descriptor of the first converted flow cytometry event data based on the first bin of the first plurality of bins; and

A second descriptor of the second converted flow cytometry event data is determined based on the second bin of the first plurality of bins.

6. The method of claim 5, wherein the first descriptor of the first converted flow cytometry event data associated with the first bin is a first bin number of the first bin of the first plurality of bins, and wherein the second descriptor of the second converted flow cytometry event data associated with the second bin is a second bin number of the first bin of the first plurality of bins.

7. The method of any one of claims 1-2, wherein the first flow cytometry event data is associated with a first rare cell and/or the second flow cytometry event data is associated with a second rare cell, optionally wherein the first rare cell and the second rare cell are cells of different cell types.

8. The method as in claim 6, comprising:

Adding the first sub-box, the first descriptor and/or the first sub-box number to a memory data structure; and

And adding the second sub-box, the second descriptor and/or the second sub-box number into the memory data structure.

9. The method as claimed in claim 8, comprising:

Converting third flow cytometry event data in the set of flow cytometry event data associated with a third event of the first plurality of events within the high-dimensional space to third converted flow cytometry event data associated with the third event within the first low-dimensional space, wherein the third event is associated with the positive subsampling requirement, and wherein the third converted flow cytometry event data is associated with a third bin of the first plurality of bins; and

Determining that the third bin associated with the third converted flow cytometry event data is the first bin associated with the first converted flow cytometry event data or the second bin associated with the second converted flow cytometry event data, wherein the third flow cytometry event data is not in the subsampled flow cytometry event data of the flow cytometry event data.

10. The method as claimed in claim 9, comprising: determining a third descriptor of the third converted flow cytometry event data based on the third bin of the first plurality of bins, wherein the third descriptor of the third converted flow cytometry event data associated with the third bin is a third bin number of the third bin of the first plurality of bins.

11. The method as claimed in claim 10, comprising: and determining that the third sub-box, the third descriptor and/or the third sub-box number are not in the memory data structure.

12. The method according to any one of claims 1-2, comprising:

Determining that fourth flow cytometry event data is associated with a negative subsampling requirement, wherein the fourth flow cytometry event data is associated with a fourth event of the first plurality of events; and

Wherein the generating includes generating the subsampled flow cytometry event data set of the flow cytometry event data including the fourth flow cytometry event data associated with the fourth event.

13. The method as claimed in claim 12, comprising:

a plurality of gates defining a plurality of cells of interest is received, wherein the fourth flow cytometry event data is associated with a cell of interest in the plurality of cells of interest.

14. The method of claim 13, wherein the fourth flow cytometry event data is associated with sorted cells.

15. The method as claimed in claim 8, comprising:

Converting third flow cytometry event data in the set of high-dimensional space associated with a second event of a second plurality of events into third converted flow cytometry event data in the first low-dimensional space associated with the second event of the second plurality of events, wherein the second event of the second plurality of events is associated with the positive subsampling requirement, wherein the third converted flow cytometry event data is associated with a third sub-bin of the first plurality of sub-bins, wherein the third sub-bin associated with the third converted flow cytometry event data and the first sub-bin associated with the first converted flow cytometry event data are the same, wherein the third converted flow cytometry event data is associated with the second event of the second plurality of events, and the first converted flow cytometry event data is associated with the first event of the first plurality of events,

Wherein the generating includes generating the subsampled flow cytometry event data set of the flow cytometry event data including the third flow cytometry event data associated with the second event of the second plurality of events.

16. The method as claimed in claim 15, comprising:

Determining that a last event of the first plurality of events is associated with a time parameter or number of events exceeding a predetermined threshold;

Resetting the memory data structure; and

Adding the third bin associated with the third converted flow cytometry event data to the in-memory data structure, wherein the third converted flow cytometry event data is associated with the second event of the second plurality of events.

17. The method as claimed in claim 16, comprising:

receiving a subsampled parameter level; and

The predetermined threshold is determined based on the subsampled parameter level.

18. The method of any one of claims 1-2, wherein converting the first flow cytometry event data comprises converting the first flow cytometry event data using a first dimension-reduction function, and/or wherein converting the second flow cytometry event data comprises converting the second flow cytometry event data using the first dimension-reduction function.

19. The method of claim 18, wherein the first dimension-reduction function is a linear dimension-reduction function.

20. The method of claim 18, wherein the first dimension-reduction function is a nonlinear dimension-reduction function.

21. The method of claim 20, wherein the nonlinear dimension reduction function is t-distribution random neighbor embedding.

22. The method as claimed in claim 18, comprising: the first dimension-reduction function or an identification thereof is received.

23. The method according to any one of claim 1 to 2,

Wherein converting the first flow cytometry event data comprises converting the first flow cytometry event data using a second dimension-reduction function to first converted flow cytometry event data associated with the first event within a second low-dimensional space, wherein the second low-dimensional space is associated with a second plurality of bins, and wherein the first converted flow cytometry event data within the second low-dimensional space is associated with a first bin of the second plurality of bins, and/or

Wherein converting the second flow cytometry event data comprises converting the second flow cytometry event data using the second dimension-reduction function to second converted flow cytometry event data associated with the second event within the second low-dimensional space, wherein the second converted flow cytometry event data within the second low-dimensional space is associated with a second bin of the second plurality of bins.

24. The method according to claim 23,

Wherein the first bin of the first plurality of bins is associated with a first cell type of interest,

Wherein the second bin of the second plurality of bins is associated with a second cell type of interest,

Wherein the second bin of the first plurality of bins is unassociated with the first cell type of interest, wherein the second bin of the first plurality of bins is unassociated with the second cell type of interest,

Wherein the first sub-tank of the second plurality of sub-tanks is unassociated with the second cell type of interest, and/or wherein the first sub-tank of the second plurality of sub-tanks is unassociated with the first cell type of interest.

25. The method of claim 24, wherein a combination of the first bin of the first plurality of bins and the first bin of the second plurality of bins is associated with a first cell type of interest, and/or wherein a combination of the second bin of the first plurality of bins and the second bin of the second plurality of bins is associated with a second cell type of interest.

26. The method of claim 25, wherein a combination of the first bin of the first plurality of bins and the second bin of the second plurality of bins is unassociated with the first cell type of interest and the second cell type of interest, and/or wherein a combination of the second bin of the first plurality of bins and the first bin of the second plurality of bins is unassociated with the first cell type of interest and the second cell type of interest.

27. The method of any of claims 1-2, wherein two bins of the first plurality of bins have the same size.

28. The method of any of claims 1-2, wherein each of the first plurality of bins has the same size.

29. The method of any of claims 1-2, wherein two bins of the first plurality of bins have different sizes.

30. The method of any one of claims 1-2, wherein two bins of the first plurality of bins contain the same number of converted flow cytometry event data.

31. The method of any one of claims 1-2, wherein each bin of the first plurality of bins contains the same number of transformed flow cytometry event data.

32. The method of any of claims 1-2, comprising determining a size of each of the first plurality of bins.

33. The method of claim 32, comprising determining the size of each bin of the first plurality of bins based on a plurality of gates.

34. The method of claim 32, comprising determining the size of each bin of the first plurality of bins based on the transformed flow cytometry event data associated with a plurality of cells of interest.

35. A computing system that subsamples flow cytometry event data, comprising: a non-transitory memory configured to store executable instructions; and

A processor in communication with the non-transitory memory, the processor programmed with the executable instructions to:

converting first flow cytometry event data in a high-dimensional space associated with a first event of a first plurality of events in a flow cytometry event dataset into first converted flow cytometry event data in a first low-dimensional space associated with the first event, wherein the first event is associated with a positive subsampling requirement,

Wherein the first low-dimensional space is associated with a first plurality of bins, and wherein the first converted flow cytometry event data is associated with a first bin of the first plurality of bins;

converting second flow cytometry event data associated with a second event of the first plurality of events within the high-dimensional space to second converted flow cytometry event data associated with the second event in the set of flow cytometry event data within the first low-dimensional space, wherein the second event is associated with the positive sub-sampling requirement, and wherein the second converted flow cytometry event data is associated with a second bin of the first plurality of bins;

Determining that the first bin associated with the first converted flow cytometry event data and the second bin associated with the second converted flow cytometry event data are different; and

A subsampled flow cytometry event data set of the flow cytometry event data is generated, the data comprising the first flow cytometry event data associated with the first event and the second flow cytometry event data associated with the second event.

36. The system of claim 35, wherein the processor is programmed with the executable instructions to: flow cytometry event data comprising the first flow cytometry event data and the second flow cytometry event data is received.

37. The system of any of claims 35-36, wherein the processor is programmed with the executable instructions to:

determining that the first flow cytometry event data of the first event of the first plurality of events is associated with the positive sub-sampling requirement; and

Determining that the second flow cytometry event data for the second event of the first plurality of events is associated with the positive sub-sampling requirement.

38. The system of any of claims 35-36, wherein the processor is programmed with the executable instructions to:

determining that the first converted flow cytometry event data is associated with the first bin of the first plurality of bins; and

Determining that the second converted flow cytometry event data is associated with the second bin of the first plurality of bins.

39. The system of any of claims 35-36, wherein the processor is programmed with the executable instructions to:

Determining a first descriptor of the first converted flow cytometry event data based on the first bin of the first plurality of bins; and

A second descriptor of the second converted flow cytometry event data is determined based on the second bin of the first plurality of bins.

40. The system of claim 39, wherein the first descriptor of the first converted flow cytometry event data associated with the first bin is a first bin number of the first bin of the first plurality of bins, and wherein the second descriptor of the second converted flow cytometry event data associated with the second bin is a second bin number of the first bin of the first plurality of bins.

41. The system of any one of claims 35-36, wherein the first flow cytometry event data is associated with a first rare cell and/or the second flow cytometry event data is associated with a second rare cell, optionally wherein the first rare cell and the second rare cell are cells of different cell types.

42. The system of claim 40, wherein the processor is programmed with the executable instructions to:

Adding the first sub-box, the first descriptor and/or the first sub-box number to a memory data structure; and

And adding the second sub-box, the second descriptor and/or the second sub-box number into the memory data structure.

43. The system of claim 42, wherein the processor is programmed with the executable instructions to:

Converting third flow cytometry event data in the set of flow cytometry event data associated with a third event of the first plurality of events within the high-dimensional space to third converted flow cytometry event data associated with the third event within the first low-dimensional space, wherein the third event is associated with the positive subsampling requirement, and wherein the third converted flow cytometry event data is associated with a third bin of the first plurality of bins; and

Determining that the third bin associated with the third converted flow cytometry event data is the first bin associated with the first converted flow cytometry event data or the second bin associated with the second converted flow cytometry event data,

Wherein the third flow cytometry event data is not in the subsampled flow cytometry event data of the flow cytometry event data.

44. The system of claim 43, wherein the processor is programmed with the executable instructions to: determining a third descriptor of the third converted flow cytometry event data based on the third bin of the first plurality of bins, wherein the third descriptor of the third converted flow cytometry event data associated with the third bin is a third bin number of the third bin of the first plurality of bins.

45. The system of claim 44, wherein the processor is programmed with the executable instructions to: and determining that the third sub-box, the third descriptor and/or the third sub-box number are not in the memory data structure.

46. The system of any of claims 35-36, wherein the processor is programmed with the executable instructions to:

Determining that fourth flow cytometry event data is associated with a negative sub-sampling requirement, wherein the fourth flow cytometry event data is associated with a fourth event of the first plurality of events,

Wherein to generate the subsampled flow cytometry event data set, the processor is programmed with the executable instructions to: generating the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the fourth flow cytometry event data associated with the fourth event.

47. The system of claim 46, wherein the processor is programmed with the executable instructions to:

a plurality of gates defining a plurality of cells of interest is received, wherein the fourth flow cytometry event data is associated with a cell of interest in the plurality of cells of interest.

48. The system of claim 47, wherein the fourth flow cytometry event data is associated with sorted cells.

49. The system of claim 42, wherein the processor is programmed with the executable instructions to:

Converting third flow cytometry event data in the set of flow cytometry event data associated with a second one of a second plurality of events within the high-dimensional space to third converted flow cytometry event data in the first low-dimensional space associated with the second one of the second plurality of events,

Wherein the second event of the second plurality of events is associated with the positive sub-sampling requirement, wherein the third converted flow cytometry event data is associated with a third bin of the first plurality of bins, wherein the third bin associated with the third converted flow cytometry event data is the same as the first bin associated with the first converted flow cytometry event data, wherein the third converted flow cytometry event data is associated with the second event of the second plurality of events, and the first converted flow cytometry event data is associated with the first event of the first plurality of events,

Wherein to generate the subsampled flow cytometry event data set, the processor is programmed with the executable instructions to: generating the subsampled flow cytometry event data set of the flow cytometry event data, the event data including the third flow cytometry event data associated with the second event of the second plurality of events.

50. The system of claim 49, wherein the processor is programmed with the executable instructions to:

Determining that a last event of the first plurality of events is associated with a time parameter or number of events exceeding a predetermined threshold;

Resetting the memory data structure; and

Adding the third bin associated with the third converted flow cytometry event data to the in-memory data structure, wherein the third converted flow cytometry event data is associated with the second event of the second plurality of events.

51. The system of claim 50, wherein the processor is programmed with the executable instructions to:

receiving a subsampled parameter level; and

The predetermined threshold is determined based on the subsampled parameter level.

52. The system of any one of claims 35-36,

Wherein to convert the first flow cytometry event data, the processor is programmed by the executable instructions to: converting the first flow cytometry event data using a first dimension-reduction function, and/or

Wherein to convert the second flow cytometry event data, the processor is programmed by the executable instructions to: converting the second flow cytometry event data using the first dimension-reduction function.

53. The system of claim 52, wherein the first dimension-reduction function is a linear dimension-reduction function.

54. The system of claim 52, wherein the first dimension-reduction function is a nonlinear dimension-reduction function.

55. The system of claim 54, wherein the nonlinear dimension reduction function is t-distribution random neighbor embedding.

56. The system of claim 52, wherein the processor is programmed with the executable instructions to: the first dimension-reduction function or an identification thereof is received.

57. The system of any one of claims 35-36,

Wherein to convert the first flow cytometry event data, the processor is programmed by the executable instructions to: converting the first flow cytometry event data into first converted flow cytometry event data associated with the first event within a second low-dimensional space using a second dimension-reduction function, wherein the second low-dimensional space is associated with a second plurality of bins, and wherein the first converted flow cytometry event data within the second low-dimensional space is associated with a first bin of the second plurality of bins, and/or

Wherein to convert the second flow cytometry event data, the processor is programmed by the executable instructions to: converting the second flow cytometry event data using the second dimension-reduction function into second converted flow cytometry event data associated with the second event within the second low-dimensional space, wherein the second converted flow cytometry event data within the second low-dimensional space is associated with a second bin of the second plurality of bins.

58. The system of claim 57,

Wherein the first bin of the first plurality of bins is associated with a first cell type of interest,

Wherein the second bin of the second plurality of bins is associated with a second cell type of interest,

Wherein the second bin of the first plurality of bins is unassociated with the first cell type of interest, wherein the second bin of the first plurality of bins is unassociated with the second cell type of interest,

Wherein the first sub-tank of the second plurality of sub-tanks is unassociated with the second cell type of interest, and/or wherein the first sub-tank of the second plurality of sub-tanks is unassociated with the first cell type of interest.

59. The system of claim 58, wherein a combination of the first sub-tank of the first plurality of sub-tanks and the first sub-tank of the second plurality of sub-tanks is associated with a first cell type of interest, and/or wherein a combination of the second sub-tank of the first plurality of sub-tanks and the second sub-tank of the second plurality of sub-tanks is associated with a second cell type of interest.

60. The system of claim 59, wherein a combination of the first sub-tank of the first plurality of sub-tanks and the second sub-tank of the second plurality of sub-tanks is unassociated with the first cell type of interest and the second cell type of interest, and/or wherein a combination of the second sub-tank of the first plurality of sub-tanks and the first sub-tank of the second plurality of sub-tanks is unassociated with the first cell type of interest and the second cell type of interest.

61. The system of any of claims 35-36, wherein two bins of the first plurality of bins have the same size.

62. The system of any of claims 35-36, wherein each of the first plurality of bins has the same size.

63. The system of any of claims 35-36, wherein two bins of the first plurality of bins have different sizes.

64. The system of any one of claims 35-36, wherein two bins of the first plurality of bins contain the same number of converted flow cytometry event data.

65. The system of any one of claims 35-36, wherein each bin of the first plurality of bins contains the same number of transformed flow cytometry event data.

66. The system of any of claims 35-36, wherein the processor is programmed with the executable instructions to: determining a size of each bin of the first plurality of bins.

67. The system of claim 66, wherein the processor is programmed with the executable instructions to: the size of each bin of the first plurality of bins is determined based on a plurality of gates.

68. The system of claim 66, wherein the processor is programmed with the executable instructions to: the size of each bin of the first plurality of bins is determined based on the transformed flow cytometry event data associated with a plurality of cells of interest.

69. The method of claim 23, wherein the second dimension-reduction function is a linear dimension-reduction function.

70. The method of claim 23, wherein the second dimension-reduction function is a nonlinear dimension-reduction function.

71. The method of claim 70, wherein the nonlinear dimension reduction function is t-distribution random neighbor embedding.

72. The system of claim 57, wherein the second dimension-reduction function is a linear dimension-reduction function.

73. The system of claim 57, wherein the second dimension-reduction function is a nonlinear dimension-reduction function.

74. The system of claim 73, wherein the nonlinear dimension reduction function is t-distribution random neighbor embedding.