Method and system for classifying fluorescence flow cytometer data - Patents.com

Abstract

Methods for classifying fluorescent flow cytometer data are provided. In some examples, the methods include processing the fluorescent flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into distinct populations according to a relationship of the data points to an associated threshold. In embodiments, the methods include determining a measure of spillover diffusion by calculating spillover diffusion coefficients and combining them into a spillover diffusion matrix. In some embodiments, the populations of the fluorescent flow cytometer data are adjusted to subtract the magnitude of the spillover diffusion. In embodiments, the spillover diffusion-adjusted populations are partitioned after potential partitions are evaluated against a threshold. In embodiments, the partitioned populations of the fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. Systems and computer-readable media for classifying fluorescent flow cytometer data are also provided.

Description

[Background technology]

[0001] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample and a sheath reservoir containing a sheath fluid. The flow cytometer transports particles (including cells) in the fluid sample as a cell stream to a flow cell, while also directing the sheath fluid to the flow cell. To characterize the components of the flow stream, the flow stream is illuminated with light. Variations in materials, such as morphology or the presence of fluorescent labels in the flow stream, can cause variations in the observed light, and these variations allow for characterization and separation.

[0002] For example, particles such as molecules in a fluid suspension, analyte-bound beads, or individual cells pass through a detection region where the particles are exposed to excitation light, typically from one or more lasers, and the light scattering and fluorescence properties of the particles are measured. The particles or components thereof are typically labeled with fluorescent dyes to facilitate detection. A large number of different particles or components can be detected simultaneously by labeling the different particles or components using spectrally distinct fluorescent dyes. In some implementations, multiple detectors are included in the analyzer, one for each scattering parameter to be measured and one or more for each distinct dye to be detected. For example, some embodiments include a spectral configuration in which more than one sensor or detector per dye is used. The data obtained includes the measured signals for each of the light scattering detectors and the fluorescent emission.

[0003] The flow cytometer may further include means for recording the measured data and analyzing the data. For example, data storage and analysis may be performed using a computer connected to the detection electronics. For example, the data may be stored in a table format, with each row corresponding to data for one particle and columns corresponding to each of the measured features. The use of a standard file format, such as the "FCS" file format, for storing data from a particle analyzer makes it easy to analyze the data using a separate program and / or machine. Using current analysis methods, the data is typically displayed in a one-dimensional histogram or two-dimensional (2D) plot for ease of visualization, although other methods may be used to visualize multidimensional data.

[0004] For example, parameters measured using a flow cytometer typically include light at the excitation wavelength scattered by the particles at a narrow angle along the approximately forward direction, referred to as forward scatter (FSC), excitation 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 at one or more detectors that measure signals over a range of spectral wavelengths, or by fluorescent dyes that are detected primarily at that particular detector or detector array. Different cell types can be distinguished by their light scattering properties and fluorescent emissions resulting from labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes.

[0005] Both flow cytometers and scanning cytometers are commercially available, for example, from BD Biosciences (San Jose, Calif.).Flow cytometry is described, for example, in Landy et al. (eds.), Clinical Flow Cytometry, Annals of the New York Academy of Sciences Volume 677 (1993); Bauer et al. (eds.), Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins (1993); Ormerod (eds.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997), and Practical Shapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003), all of which are incorporated herein by reference. Fluorescence imaging microscopy is described, for example, in Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd Edition, Plenum Press (1989), incorporated herein by reference.

[0006] After flow cytometer data is received from one or more detectors, a data analysis process often takes place to allow the user to understand the data. In some cases, flow cytometer data analysis involves determining the phenotype associated with the analytes (e.g., cells, particles) being illuminated in the flow cytometer. For the majority of all cytometry samples, a subset of cell types (i.e., phenotypes) must be identified before detailed analysis is possible. In a typical workflow, a pre-planned gating hierarchy summarizes universally accepted definitions of cell types and must be manually adjusted to fit the data of each sample (to account for biological effects, batch effects, and any other idiosyncrasies). Adjustment approaches can be either "unsupervised" or "supervised." Unsupervised approaches begin with clustering the flow cytometer data into populations, then attempt to assign a cell type label (i.e., phenotype) to each population cluster using some a priori knowledge of the association of specific analytes with specific parameters (e.g., fluorescence). Supervised approaches, on the other hand, typically start by establishing a gating hierarchy, i.e., a set of rules governing how individual cells are classified (i.e., phenotyped) based on their state on one or more parameters. Flow cytometer data is then "fitted" to the gating hierarchy so that individual data points fall somewhere on the hierarchy. The DAFi algorithm, for example, fits clusters of events based on their centroids into an existing gating tree, which allows cell types to be defined along natural boundaries in the data rather than fixed gate boundaries.The DAFi algorithm is described, for example, in Lee et al. (2018). DAFI: a directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data. Cytometry Part A, 93(6), 597-610, which is incorporated herein by reference. FlowDensity, on the other hand, finds density troughs in the data to use as gating boundaries for predefined hierarchies. The FlowDensity algorithm is described, for example, in Malek et al. (2015). flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics, 31(4), 606-607, which is incorporated herein by reference. However, the DAFi algorithm can only account for small sample-to-sample variations, and the FlowDensity algorithm requires manual automated phenotyping experts to tune for each panel.

[0007] The Ek' Balam algorithm represents an approach to flow cytometer data conditioning that improves upon the DAFi and FlowDensity algorithms in that the Ek' Balam algorithm is easy to tune and can handle large sample-to-sample variability. However, flow cytometry data analysis protocols that involve the measurement of more than one parameter (e.g., fluorescent signal), such as the Ek' Balam algorithm, are complicated by spillover, a phenomenon in which particulate-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, light can "spillover" and be detected by off-target detectors. Spillover can be corrected by unmixing, in which new per-fluorochrome intensity values ​​are calculated by solving simultaneous equations that relate fluorochrome intensity values ​​to measured detector values ​​via the level of observed spillover. Unmixing is often referred to as "compensation" when the number of detectors is equal to the number of fluorochromes being unmixed. While unmixing corrects for the intensity contributions from each fluorochrome to the other fluorochromes, it fails to correct for noise contributions, i.e., errors contributed to the fluorescent flow cytometer data by spillover. This noise is referred to as "spillover diffusion." In some instances, the spillover diffusion noise is constructive, resulting in a higher signal intensity than would otherwise be observed, and in other instances, the noise is destructive, resulting in a lower intensity. For example, FIG. 1 demonstrates how population 101 diffuses due to the presence of noise from fluorochrome A on the detector used to measure fluorochrome B. Due to population diffusion, classification (i.e., phenotyping) of the fluorescent flow cytometer data by data analysis protocols can be inaccurate. For example, FIG. 2A and FIG. 2B show the effect of spillover diffusion on the performance of the Ekbalam algorithm to accurately distinguish between separate population clusters of flow cytometer data. As shown in FIG. 2B, population 101 is inaccurately partitioned, with some of the populations being assigned to incorrect phenotypes as a result (i.e., A, B, C, D, E ... + B - Instead, A+ B + Thus, there is a need for a solution to spillover diffusion in flow cytometer data analysis. Summary of the Invention

[0008] Aspects of the invention include methods for classifying fluorescent flow cytometer data. In some embodiments, the method includes processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into populations. In embodiments, the fluorescent flow cytometer data is clustered based on the status of each data point relative to a hierarchy. In such embodiments, the fluorescent flow cytometer data is clustered into populations based on the positivity or negativity of the fluorescent flow cytometer data for a particular fluorochrome. In some embodiments, the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorochrome based on a relationship of the fluorescent flow cytometer data to a threshold value. After the data is clustered, embodiments of the method include determining a measure of spillover diffusion. In some embodiments, determining the spillover diffusion includes calculating a spillover diffusion coefficient that quantifies the extent to which the intensity of light collected from a first fluorochrome by a first detector is affected by the simultaneous collection of light from a second fluorochrome by the same detector. In embodiments, a spillover diffusion coefficient is calculated for each possible combination of fluorescent detector and fluorochrome to determine the extent to which light emitted from a particular fluorochrome is collected at a given detector. In some embodiments, the spillover diffusion coefficients are combined in a spillover diffusion matrix. In certain embodiments, the populations of the fluorescence flow cytometer data are adjusted to account for the magnitude of the determined spillover diffusion in a spillover diffusion matrix. Performing the method may further include partitioning the separate spillover diffusion adjusted populations of the fluorescence flow cytometer data. In embodiments, partitioning the populations involves calculating a Matthew's correlation coefficient and establishing partitions that delineate between the separate populations (i.e., populations exhibiting different fluorescence parameters) in a manner that optimizes the Matthew's correlation coefficient for each associated threshold. In embodiments, the partitioned populations of the fluorescence flow cytometer data are then classified such that the separate populations are associated with respective subtypes (i.e., phenotypes).

[0009] Aspects of the invention also include a system for classifying fluorescent flow cytometer data. In some embodiments, the system includes a particle analyzer configured to generate the fluorescent flow cytometer data. The system may also include a processor having a memory operatively coupled to the processor, the memory including instructions stored in the memory, which when executed by the processor, cause the processor to process the flow cytometer data using a supervised algorithm configured to cluster the fluorescent flow cytometer data into populations. In embodiments, the fluorescent flow cytometer data is clustered based on a status of each data point relative to a hierarchy. In such embodiments, the fluorescent flow cytometer data is clustered into populations based on the positivity or negativity of the fluorescent flow cytometer data for a particular fluorochrome. In some embodiments, the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorochrome based on a relationship of the fluorescent flow cytometer data to a threshold value. After the data is clustered, the processor may be configured to determine a measure of spillover diffusion. In some embodiments, determining the spillover diffusion includes calculating a spillover diffusion coefficient that quantifies the extent to which the intensity of light collected from a first fluorochrome by a first detector is affected by the simultaneous collection of light from a second fluorochrome by the same detector. In embodiments, a spillover diffusion coefficient is calculated for each possible combination of fluorescent detector and fluorescent dye to determine the extent to which light emitted from a particular fluorescent dye is collected at a given detector. In some embodiments, the processor is configured to combine the spillover diffusion coefficients in a spillover diffusion matrix. In certain embodiments, the populations of the fluorescent flow cytometer data are adjusted to account for the determined magnitude of spillover diffusion in the spillover diffusion matrix. The processor may be further configured to partition the separate spillover diffusion adjusted populations of the fluorescent flow cytometer data.In an embodiment, partitioning the populations involves calculating Matthew's correlation coefficient and establishing partitions that delineate between the distinct populations (i.e., populations exhibiting different fluorescent parameters) in a manner that optimizes the Matthew's correlation coefficient for each associated threshold. In an embodiment, the partitioned populations of fluorescent flow cytometer data are then classified such that the distinct populations are associated with respective subtypes (i.e., phenotypes).

[0010] Aspects of the present disclosure further include a computer control system, the system further comprising one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program, when loaded into the computer, includes instructions for clustering the fluorescent flow cytometer data into populations according to one or more different parameters (i.e., fluorescent dyes), determining the spillover diffusion between each detector-parameter pair (i.e., by calculating the spillover diffusion coefficient), creating a spillover diffusion matrix showing how the detection of a particular fluorescent dye by its corresponding detector is affected by spillover from other fluorescent dyes, adjusting the fluorescent flow cytometer data to compensate for the spillover diffusion by subtracting the magnitude of the spillover diffusion determined by the spillover diffusion matrix, evaluating the quality of the different partitions separating the distinct populations of the fluorescent flow cytometer data by calculating the Matthew correlation coefficient against a threshold value that distinguishes between populations that are positive for a given parameter and populations that are negative for a given parameter, and classifying (i.e., phenotyping) the adjusted populations of the fluorescent flow cytometer data. [Brief description of the drawings]

[0011] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawing figures, which include:

[0012] [Figure 1] FIG. 1 shows spillover diffusion in fluorescent flow cytometer data. [Figure 2A] FIG. 1 illustrates how spillover diffusion impedes the ability of an algorithm to accurately distinguish between distinct population clusters of fluorescent flow cytometer data. [Figure 2B] FIG. 1 illustrates how spillover diffusion impedes the performance of algorithms that classify populations (i.e., phenotypes) of fluorescent flow cytometer data. [Diagram 3] FIG. 1 illustrates a hierarchy for phenotyping T cells by determining whether the cells are positive or negative for the presence of CD4 or CD8. [Figure 4] FIG. 1 shows clustering of fluorescent flow cytometer data against thresholds. [Diagram 5] FIG. 1 illustrates a spillover diffusion matrix. [Figure 6] FIG. 1 illustrates a process for adjusting populations of flow cytometer data based on spillover diffusion. [Figure 7A] FIG. 13 shows flow cytometer data adjusted based on spillover diffusion. [Figure 7B] FIG. 1 shows classification (i.e., phenotyping) of flow cytometer data adjusted based on spillover diffusion. [Figure 8] FIG. 1 illustrates a flow cytometer in accordance with certain embodiments. [Figure 9] FIG. 2 illustrates an example functional block diagram of a processor in accordance with certain embodiments. [Figure 10] FIG. 1 is a block diagram illustrating a computing system in accordance with certain embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] A method for classifying fluorescent flow cytometer data is provided. In some examples, the method includes processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into separate populations according to the relationship of the data points to an associated threshold value. In an embodiment, the method includes determining a measure of spillover diffusion by calculating spillover diffusion coefficients and combining them into a spillover diffusion matrix. In some embodiments, the populations of the fluorescent flow cytometer data are adjusted to subtract a magnitude of spillover diffusion determined by the spillover diffusion matrix. In an embodiment, the spillover diffusion adjusted populations are partitioned after the potential partitions are evaluated against the threshold value. In an embodiment, the partitioned populations of the fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. A system and computer readable medium for classifying fluorescent flow cytometer data are also provided.

[0014] Before the present invention is described in more detail, it is to be understood that the invention is not limited to particular embodiments described, which may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, since the scope of the present invention is limited only by the appended claims.

[0015] Where a range of values ​​is provided, it is understood that each intermediate value between the upper and lower limits of that range, to the tenth of the unit of the lower limit, and any other stated or intermediate value within this stated range, is encompassed within the invention, unless the context clearly dictates otherwise. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where a stated range includes one or both of the limits, ranges excluding either or both of those included limits are also encompassed within the invention.

[0016] Certain ranges are presented herein with numerical values ​​preceded by the term "about". The term "about" is used herein to provide literal support for the exact number it precedes, as well as the number that is close to or approximately the number it precedes. When determining whether a number is close to or approximately a specifically recited number, the near or approximately unrecited number may be a number that provides the substantial equivalent of the specifically recited number in the context in which it is presented.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are described below.

[0018] All publications and patents cited herein are incorporated by reference as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0019] It should be noted that, as used herein and in the appended claims, the articles "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional elements. Thus, this statement is intended to serve as a predicate for use of exclusive terminology such as "solely" and "only" in connection with the recitation of claim elements, or for use of a "negative" limitation.

[0020] As will be apparent to those skilled in the art upon reading this disclosure, each of the separate embodiments described and illustrated herein has separate components and features which may be readily separated from or combined with the features of any of the other various embodiments without departing from the scope or spirit of the invention. Any recited method may be carried out in the order of events recited or in any other order which is logically possible.

[0021] Although the systems and methods have been or will be described with functional descriptions for grammatical fluidity, it is expressly understood that unless expressly recited under 35 U.S.C. ...

[0022] Method for classifying fluorescence flow cytometer data - Patents.com As discussed above, aspects of the present disclosure include methods for classifying fluorescent flow cytometer data. By "fluorescent flow cytometer data" is meant information about parameters of a sample (e.g., cells, particles) in a flow cell collected by any number of fluorescent detectors in a particle analyzer. In embodiments, the fluorescent flow cytometer data includes signals from a plurality of different fluorochromes (e.g., ranging from 2-20 different fluorochromes, including 3-5 different fluorochromes). In some embodiments, the plurality of different fluorochromes includes 2 or more different fluorochromes, including 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, and 20 or more different fluorochromes. The fluorescent flow cytometer data can be obtained by any convenient protocol, including those described below.

[0023] In some embodiments, the method includes generating one or more population clusters based on determined parameters (e.g., fluorescence) of analytes (e.g., cells, particles) in the sample. As used herein, a "population" or "subpopulation" of analytes (e.g., cells or other particles) generally refers to a group of analytes having characteristics (e.g., optical properties, impedance properties, or time properties) related to one or more measured parameters such that the measured fluorescence parameter data form a cluster in data space. Thus, a population is recognized as a cluster in the data. Each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte, although conversely, clusters corresponding to noise or background are also typically observed. Clusters can be defined in terms of a subset of measured fluorescence parameters (i.e., fluorescent dyes) that correspond to populations that differ only within a subset of dimensions, e.g., within a subset of measured parameters or features extracted from the measurements of the sample.

[0024] Aspects of the invention include supervised clustering of fluorescent flow cytometer data. Any convenient supervised phenotyping algorithm may be used for this step. In some embodiments, the supervised phenotyping algorithm is the Ekbalam algorithm. The Ekbalam algorithm is described in Amir et al. (2019). Development of a comprehensive antibody staining database using a standardized analytics pipeline. Frontiers in immunology, 10, 1315, which is incorporated herein by reference. Thus, in certain embodiments, a population of fluorescent flow cytometer data is clustered based on the status of each data point relative to a hierarchy. The hierarchy described herein defines the criteria by which the fluorescent flow cytometer data is classified into a particular population. In some embodiments, the hierarchy establishes how data points that are positive or negative for the same parameter are grouped together. For example, FIG. 3 illustrates a hierarchy for clustering T cells by determining whether the cells are positive or negative for the presence of CD4 and CD8. Cells that are positive for CD4 but negative for CD8 are "CD4 T cells," while cells that are positive for both markers are "double positive T cells," and so on.

[0025] In some embodiments, clustering the fluorescent flow cytometer data includes comparing the fluorescent flow cytometer data to a threshold value. By "threshold value" is meant a value that distinguishes between positive / negative fluorescent flow cytometer data for a particular fluorochrome. In other words, fluorescent flow cytometer data above the threshold value may be described as positive for a particular fluorochrome, while fluorescent flow cytometer data below the threshold value may be described as negative for the same fluorochrome. For example, FIG. 4 illustrates how flow cytometer data is clustered according to a threshold value that distinguishes between positive and negative data. Threshold value 401 distinguishes flow cytometer data that is positive for CD8 from flow cytometer data that is negative for CD8. Similarly, threshold value 402 distinguishes flow cytometer data that is positive for CD4 from flow cytometer data that is negative for CD4. The clustering can be performed by any convenient method. In some embodiments, the clustering is performed by a FlowSOM algorithm. FlowSOM is described in Van Gassen et al. (2015). FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry Part A, 87(7), 636-645, which is incorporated herein by reference.In other embodiments, the clustering may be performed using a SOM as described in Kohonen, The self-organizing map, Proceedings of the IEEE (1990) 78: 1464-1480; K-means as described in Hartigan & Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Journal of the Royal Statistical Society. Series C (Applied Statistics) (1979) 28: pp. 100-108; Gaussian Mixture Models as described in Sanderson & Curtin, An open source C++ implementation of multi-threaded Gaussian mixture models, k-means and expectation maximisation, 2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS) (https: / / doi.org / 10.1109 / ICSPCS.2017.8270510); cytometry data using FlowGrid, BMC Syst Biol 13, 35 (2019). https: / / doi.org / 10.1186 / s12918-019-0690-2, or X-shift, Samusik et al., Automated mapping of phenotype space with single-cell data, Nat Methods (2016) 13: 493-496.

[0026] After the flow cytometer data has been clustered, an embodiment of the present invention includes determining a measure of spillover diffusion for the population of fluorescent flow cytometer data. As discussed above and illustrated in FIGS. 1 and 2A-2B, fluorescent flow cytometer data at a collection point (i.e., a point received by one or more fluorescent detectors) is subject to spillover diffusion. Spillover is a phenomenon in which particle-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, the light can "spillover" and be detected by off-target detectors. Thus, spillover diffusion is noise present in the fluorescent flow cytometer data caused by spillover. Thus, in some embodiments, unconditioned flow cytometer data is falsely detected due to unintended detection of light of a particular wavelength by one or more detectors. In certain embodiments, determining the measure of spillover diffusion includes quantifying the extent to which fluorescent flow cytometer data collected from a first fluorochrome by a first detector is affected by simultaneous collection of light from a second fluorochrome by the same detector. In some examples, the fluorescence flow cytometer data that is subject to spillover spreading is affected by a higher signal intensity than would otherwise be observed (i.e., the spillover spreading noise is constructive). In other examples, the fluorescence flow cytometer data that is subject to spillover spreading is affected by a lower signal intensity than would otherwise be observed (i.e., the spillover spreading noise is destructive). In certain embodiments, determining the measure of spillover spreading includes calculating a spillover diffusion coefficient. Spillover diffusion coefficients are described in Nguyen et al. (2013). Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry Part A, 83(3), 306-315, the disclosures of which are incorporated herein by reference. In some embodiments, the spillover diffusion coefficient is calculated according to the following formula:

[0027]

number

[0028] In the above formula, SS is the spillover diffusion coefficient, and Δσ f is the incremental standard deviation indicating the spread of emission between the positive and negative fluorescence flow cytometer data collected from the fluorochrome, and Δd is the difference in intensity of fluorescence between the positive and negative fluorescence flow cytometer data received by the fluorescence detector.

[0029] Thus, the spillover diffusion coefficient measures the degree to which the fluorescence flow cytometer data collected by a given fluorescence detector is affected by the presence of light associated with a particular fluorochrome. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed to the fluorescence flow cytometer data by light emitted from the associated fluorochrome being collected by the given detector. In embodiments, a higher spillover diffusion coefficient corresponds to more spillover diffusion for a given fluorochrome-detector pair.

[0030] In some embodiments, determining the measure of spillover diffusion also includes calculating a spillover diffusion coefficient for each possible fluorescent detector-fluorochrome combination so that it can be determined how the fluorescent flow cytometer data collected at each detector is affected by the presence of light associated with each fluorochrome. In embodiments, the calculated spillover diffusion coefficients for each fluorescent detector-fluorochrome pair are combined in a spillover diffusion matrix. In certain embodiments, the spillover diffusion matrix demonstrates how the detection of a particular fluorochrome by its corresponding detector is affected by spillover from other fluorochromes. For example, FIG. 5 presents one embodiment of a spillover diffusion matrix that provides spillover diffusion coefficients for 23 different fluorochromes. Each column in the matrix corresponds to a detector configured to detect one of the 23 different fluorochromes, and each row in the matrix corresponds to a parameter of the flow cytometer data that is detected. The cells where the columns and rows intersect are filled with the calculated spillover diffusion coefficient for the fluorescent detector-fluorochrome pair that indicates the degree to which the fluorochrome in question contributes error to the associated detector. The total extent to which a fluorochrome causes spillover diffusion can be approximated by summing all the values ​​in that row, and the total extent to which a detector is affected by spillover diffusion can be calculated by summing all the values ​​in that column. In some embodiments, the spillover diffusion coefficients are summed to calculate the total diffusion effect (i.e., the cumulative effect of spillover diffusion on a particular subset of fluorescent flow cytometer data).

[0031] In some embodiments, the spillover diffusion matrix is ​​calculated by an AutoSpread algorithm. The AutoSpread algorithm was created by Becton Dickinson and is described in U.S. Provisional Application No. 63 / 020,758, which is incorporated herein by reference, and is configured to create a spillover diffusion matrix (e.g., as described above) without requiring a distinction between a population of flow cytometers that are positive and a population of flow cytometers that are negative for a given fluorochrome. AutoSpread characterizes the diffusion contributed to the detection signal of a first fluorochrome by including a second fluorochrome in the same flow cytometry panel. AutoSpread generates one coefficient for each interaction between the fluorescent detector and the fluorochrome, and arranges the coefficients in a matrix similar to the spillover diffusion matrix described above. In embodiments, calculating the spillover diffusion coefficient includes assuming that the intensity of the fluorescence collected by the fluorescent detector for the negative population of the flow cytometer data is 0 and the corresponding standard deviation is an unknown quantity. In some embodiments, the spillover diffusion coefficient is calculated according to the following formula:

[0032]

number

[0033] In the above formula, SS is the spillover diffusion coefficient, and σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, and σ0 2 is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data and d is the intensity of the light collected by the fluorescent detector.

[0034] In some embodiments, the flow cytometer data (σ 2To obtain an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data when the intensity of the fluorescence collected by the fluorescence detector for the negative population of (a) is assumed to be zero, the spillover diffusion coefficient is calculated according to a sequence of linear regressions. The fluorescent flow cytometer data is first sorted into quantiles according to the intensity values ​​detected by the fluorescent detector. The number of quantiles is 256 by default, but is adjusted downward to around 8 to ensure that each quantile has a sufficient number of data points to allow a reliable estimate of the standard deviation. The robust standard deviation of the light emitted from the fluorochrome is then regressed against the square root of the median intensity of the light detected for each quantile. The y-intercept of the ordinary least squares fit is obtained as an estimate of the standard deviation in the negative population of the flow cytometer data when the intensity of the light detected for the negative population is assumed to be zero. The estimate of the standard deviation of the light emitted from the fluorochrome is used to obtain a new zero-adjusted standard deviation. The zero-adjusted standard deviation for the fluorochrome is regressed against the square root of the median fluorescence of each quantile detected by the fluorescent detector. The slope of the ordinary least squares fit (calculated by Equation 2) is taken as the spillover diffusion coefficient.

[0035] Aspects of the present disclosure further include adjusting the fluorescent flow cytometer data to account for spillover diffusion. By "adjusting" it is meant modifying the data to more accurately quantify the presence of fluorochromes in a sample (e.g., cells, particles) being illuminated in a flow cell. In some embodiments, the fluorescent flow cytometer data is adjusted to remove substantially all constructive errors resulting from spillover diffusion. In embodiments, adjusting the fluorescent flow cytometer data includes generating a separate spillover diffusion adjusted population. In certain embodiments, generating a separate spillover diffusion adjusted population includes subtracting a magnitude of spillover diffusion from a relevant population of the flow cytometer data, i.e., counteracting the constructive effects of signals affected by spillover diffusion. In certain embodiments, the magnitude of spillover diffusion is determined from a spillover diffusion matrix. In some embodiments, adjusting the flow cytometer data includes subtracting a total diffusion effect from a relevant portion of the flow cytometer data. For example, FIG. 6 illustrates adjustment of the flow cytometer data such that spillover diffusion is accounted for. Arrow 601 illustrates how the medians of the population clusters are shifted downwards. In other words, the magnitude of spillover diffusion is subtracted from the data points in a population cluster, thereby changing the position of the cluster relative to other population clusters. Figure 7A shows flow cytometer data adjusted by the above-mentioned process. In some embodiments, the fluorescent flow cytometer data is adjusted by calculating the likelihood that each cell will exceed a threshold according to all known noise sources, including spillover diffusion.

[0036] After the populations of fluorescent flow cytometer data have been adjusted for spillover diffusion (e.g., as described above), embodiments of the invention further include partitioning the populations of spillover diffusion adjusted fluorescent flow cytometer data. By "partition" is meant delineating between populations of flow cytometer data that have been determined to be separate based on positivity or negativity for a number of different fluorochromes. In other words, partitions are made to formally distinguish between populations of fluorescent flow cytometer data that are classified differently (e.g., representing different phenotypes).

[0037] In an embodiment, partitioning the distinct spillover diffusion-adjusted populations of the flow cytometer data includes calculating a Matthew correlation coefficient (MCC). This coefficient is a measure of the quality of the binary classification and provides a quantification of how accurately a given threshold value distinguishes between positive and negative groups of the flow cytometer data. Matthew correlation coefficients are described in Matthews, BW (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure, 405(2), 442-451, the disclosures of which are incorporated herein by reference. In an embodiment, the potential partitioning between the distinct spillover diffusion-adjusted populations of the fluorescent flow cytometer data is evaluated for each of the threshold values. In such an embodiment, a Matthew correlation coefficient is calculated for each of the threshold values, thereby evaluating the level of agreement between the threshold values ​​and the partitioning. Thus, an embodiment of the present invention includes partitioning the populations such that the Matthew's correlation coefficient is optimized with respect to an associated threshold value that distinguishes between positive and negative populations of the flow cytometer data for a particular fluorochrome. In other words, partitioning the fluorescent flow cytometer data includes maximizing the degree to which separate populations (i.e., populations exhibiting different combinations of fluorescent parameters) are distinguished from one another as determined by their relationship to an associated threshold value (e.g., quantified by the Matthew's correlation coefficient). In an embodiment, this process is iteratively determined to determine the optimal partition that distinguishes between positive and negative populations for each associated parameter of the flow cytometer data. In an embodiment, the Matthew's correlation coefficient is calculated according to the following formula:

[0038]

number

[0039] In the above formula, MCC is Matthew's correlation coefficient, TP represents true positive events, TN represents true negative events, FP represents false positive events, and FN represents false negative events.

[0040] According to the present disclosure, a true positive event is fluorescent flow cytometer data that is evaluated as positive for a particular fluorescent dye by both the threshold and the category, a true negative event is fluorescent flow cytometer data that is evaluated as negative for a particular fluorescent dye by both the threshold and the category, a false positive event is fluorescent flow cytometer data that is positive for a particular fluorescent dye by the category and negative by the threshold, and a false negative event is fluorescent flow cytometer data that is negative for a particular fluorescent dye by the category and positive by the threshold.

[0041] In some embodiments of the present invention, the fluorescence flow cytometer data does not include a signal that is positive for a particular fluorochrome. In other words, no fluorescence emitted from the fluorochrome is detected. In such embodiments, partitioning the separate spillover diffusion adjusted fluorescence flow cytometer data may include calculating a balanced precision for each partition as a surrogate metric for determining the optimal partition. The balanced precision is the average of the precision for determining positive events and the precision for determining negative events, and is calculated according to the following formula:

[0042]

number

[0043] In the above formula, BA is the balanced accuracy, TP represents true positive events, TN represents true negative events, FP represents false positive events, and FN represents false negative events.

[0044] An embodiment of the present invention also includes classifying the partitioned populations of the fluorescent flow cytometer data, i.e., determining the subtype of cells or particles designated by each distinct spillover diffusion adjusted population of the flow cytometer data. In an embodiment, the classification is determined based on a hierarchy (described above). Thus, a population of fluorescent flow cytometer data partitioned (e.g., as described above) is assigned a classification (i.e., phenotyped) based on the combination of fluorescent parameters it exhibits. For example, FIG. 7B illustrates how the spillover diffusion adjusted fluorescent flow cytometer data shown in FIG. 7A is partitioned and the distinct population clusters are correctly assigned to their respective phenotypes. As shown in FIG. 2B, as discussed in the introduction section, some of the population clusters 101 would be assigned an incorrect phenotype without data adjustment. However, after the populations are adjusted for spillover diffusion (e.g., as shown in FIG. 6), the entire population 701 is assigned the correct phenotype (i.e., A), as opposed to only a portion of the population 701 being assigned the correct phenotype. + B - ).

[0045] As summarized above, the fluorescence data used in the methods of the present invention can be obtained using any convenient protocol. In some embodiments, a sample having particles is illuminated with a light source, light from the sample is detected, and a population of relevant particles is generated based at least in part on the measurements of the detected light. In some examples, the sample is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, a plant, a fungus, or a subset of animal tissues, cells, or components that may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, for example, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. The biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or its derivatives, e.g., plasma, tears, urine, semen, etc., and in some examples, the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0046] In certain embodiments, the source of the sample is a "mammal" or "mammalian," terms used broadly to refer to organisms within the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders, at any stage of development (i.e., neonates, infants, juveniles, adolescents, adults), and in certain embodiments, the human subjects are juveniles, adolescents, or adults. It should be understood that the present invention may be applied to samples from human subjects, but may also be practiced on samples from other animal subjects (i.e., within "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0047] In carrying out the methods of the invention, a sample having particles (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide range of wavelengths, e.g., over 100 nm, e.g., over 150 nm, e.g., over 200 nm, e.g., over 250 nm, e.g., over 300 nm, e.g., over 350 nm, e.g., over 400 nm, and over 500 nm, including over 50 nm. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes illuminating with a broadband light source, the subject broadband light source protocol may include, but is not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, continuous spectrum broadband LEDs, superluminescent diodes, semiconductor light emitting diodes, wide spectrum LED white light sources, multi-LED integrated white light sources, among other broadband light sources or any combination thereof.

[0048] In other embodiments, the method includes illuminating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, such as a light source that emits light in a narrow wavelength range, such as a range of 50 nm or less, including light sources that emit specific wavelengths of light (i.e., monochromatic light), such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less, and light sources that emit specific wavelengths of light (i.e., monochromatic light). When the method includes illuminating with a narrowband light source, the subject narrowband light source protocol may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0049] Aspects of the invention include collecting the fluorescence using a fluorescence detector. The fluorescence detector may, in some examples, be configured to detect fluorescent emission from fluorescent molecules associated with particles in the flow cell, e.g., labeled specific binding members (such as labeled antibodies that specifically bind to a marker of interest). In certain embodiments, the method includes detecting fluorescence from the sample using one or more fluorescence detectors, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., fifteen or more, and twenty-five or more fluorescence detectors. In embodiments, each of the fluorescence detectors is configured to generate a fluorescence data signal. Fluorescence from the sample may be detected by each fluorescence detector independently over one or more of a wavelength range of 200 nm to 1200 nm. In some examples, the method includes detecting fluorescence from the sample over a wavelength range including 600 nm to 800 nm, such as 200 nm to 1200 nm, such as 300 nm to 1100 nm, such as 400 nm to 1000 nm, such as 500 nm to 900 nm. In other examples, the method includes detecting fluorescence with each fluorescence detector at one or more specific wavelengths. For example, the fluorescence may be detected at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof, depending on the number of different fluorescence detectors in the optical detection system of the present invention. In certain embodiments, the method includes detecting wavelengths of light corresponding to the fluorescence peak wavelengths of specific fluorescent dyes present in the sample. In an embodiment, the fluorescent flow cytometer data is received from one or more fluorescent detectors (e.g., one or more detection channels), such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, or even eight or more fluorescent detectors (e.g., eight or more detection channels).

[0050] System for classifying fluorescence flow cytometer data - Patents.com Aspects of the present disclosure include a system for classifying fluorescent flow cytometer data. In embodiments, the fluorescent flow cytometer data is clustered, adjusted for spillover diffusion, and partitioned such that distinct populations are classified differently. In some embodiments, the system includes a particle analyzer configured to generate the fluorescent flow cytometer data and a processor configured to analyze the fluorescent flow cytometer data.

[0051] In some embodiments, the particle analyzer has a flow cell and a laser configured to illuminate the particles in the flow cell. In embodiments, the laser may be any convenient laser, such as a continuous wave laser. For example, the laser may be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near infrared diode laser. In other embodiments, the laser may be a helium neon (HeNe) laser. In some examples, the laser is a gas laser, such as a helium neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO2 laser, an argon fluorine (ArF) excimer laser, a krypton fluorine (KrF) excimer laser, a xenon chlorine (XeCl) excimer laser, or a xenon fluorine (XeF) excimer laser, or a combination thereof. In other examples, the flow cytometer includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the laser of interest includes a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In yet another example, the flow cytometer of the present invention includes a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a slim YAG laser, a ytterbium YAG laser, a Y2O3 laser, or a cerium-doped laser, and combinations thereof.

[0052] Aspects of the invention also include a forward scatter detector configured to detect forward scattered light. The number of forward scatter detectors in the flow cytometer of the invention can vary as needed. For example, the particle analyzer may include one forward scatter detector, or multiple forward scatter detectors, including two or more, three or more, four or more, and five or more. In certain embodiments, the flow cytometer includes one forward scatter detector. In other embodiments, the flow cytometer includes two forward scatter detectors.

[0053] Any convenient detector for detecting the collected light may be used in the forward scatter detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector is a 0.01 cm 2 ~10cm 2 , e.g. 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 The range is 1 cm 2 ~5cm 2 a photomultiplier tube such as a photomultiplier tube having an active detection surface area for each region including

[0054] When the particle analyzer includes multiple forward scatter detectors, each detector may be the same, or the set of detectors may be a combination of different types of detectors. For example, when the particle analyzer includes two forward scatter detectors, in some embodiments, the first forward scatter detector is a CCD type device and the second forward scatter detector (or imaging sensor) is a CMOS type device. In other embodiments, both the first and second forward scatter detectors are CCD type devices. In yet other embodiments, both the first and second forward scatter detectors are CMOS type devices. In yet other embodiments, the first forward scatter detector is a CCD type device and the second forward scatter detector is a photomultiplier tube (PMT). In yet other embodiments, the first forward scatter detector is a CMOS type device and the second forward scatter detector is a photomultiplier tube. In yet other embodiments, both the first and second forward scatter detectors are photomultiplier tubes.

[0055] In embodiments, the forward scatter detector is configured to measure light continuously or at discrete intervals. In some examples, the target detector is configured to make measurements of collected light continuously. In other examples, the target detector is configured to measure at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0056] Embodiments of the invention also include a light dispersion / separation module positioned between the flow cell and the forward scatter detector. Light dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices. In some embodiments, a bandpass filter is positioned between the flow cell and the forward scatter detector. In other embodiments, two or more bandpass filters are positioned between the flow cell and the forward scatter detector, including, for example, two or more, three or more, four or more, five or more. In embodiments, the bandpass filter has a minimum bandwidth in the range of 2 nm to 100 nm, such as 3 nm to 95 nm, such as 5 nm to 95 nm, such as 10 nm to 90 nm, such as 12 nm to 85 nm, such as 15 nm to 80 nm, including a bandpass filter having a minimum bandwidth in the range of 20 nm to 50 nm, and reflects light having other wavelengths to the forward scatter detector.

[0057] Certain embodiments of the invention include a side scatter detector configured to detect side scattered wavelengths of light (e.g., light refracted and reflected from the surface and internal structures of a particle). In other embodiments, the flow cytometer includes multiple side scatter detectors, such as two or more, such as three or more, such as four or more, and including five or more side scatter detectors.

[0058] Any convenient detector for detecting the collected light may be used in the side scatter detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector is a 0.01 cm 2 ~10cm 2 , e.g. 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 The range is 1 cm 2 ~5cm 2 a photomultiplier tube such as a photomultiplier tube having an active detection surface area for each region including

[0059] When the particle analyzer includes multiple side scatter detectors, each side scatter detector may be the same, or the set of side scatter detectors may be a combination of different types of detectors. For example, when the particle analyzer includes two side scatter detectors, in some embodiments, the first side scatter detector is a CCD type device and the second side scatter detector (or imaging sensor) is a CMOS type device. In other embodiments, both the first and second side scatter detectors are CCD type devices. In still other embodiments, both the first and second side scatter detectors are CMOS type devices. In still other embodiments, the first side scatter detector is a CCD type device and the second side scatter detector is a photomultiplier tube (PMT). In still other embodiments, the first side scatter detector is a CMOS type device and the second side scatter detector is a photomultiplier tube. In still other embodiments, both the first and second side scatter detectors are photomultiplier tubes.

[0060] Embodiments of the invention also include a light dispersion / separation module positioned between the flow cell and the side scatter detector. Light dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices.

[0061] In embodiments, the particle analyzer also includes a fluorescence detector configured to detect light at one or more fluorescent wavelengths, hi other embodiments, the particle analyzer includes a plurality of fluorescence detectors, including 2 or more, 3 or more, 4 or more, 5 or more, 10 or more, 15 or more, etc., and 20 or more.

[0062] Any convenient detector for detecting collected light may be used in the fluorescence detectors described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector is a 0.01 cm 2 ~10cm 2 , e.g. 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 The range is 1 cm 2 ~5cm 2 a photomultiplier tube such as a photomultiplier tube having an active detection surface area for each region including

[0063] When the particle analyzer includes multiple fluorescence detectors, each fluorescence detector may be the same, or the set of fluorescence detectors may be a combination of different types of detectors. For example, when the particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector (or imaging sensor) is a CMOS type device. In other embodiments, both the first and second fluorescence detectors are CCD type devices. In yet other embodiments, both the first and second fluorescence detectors are CMOS type devices. In yet other embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector is a photomultiplier tube (PMT). In yet other embodiments, the first fluorescence detector is a CMOS type device and the second fluorescence detector is a photomultiplier tube. In yet other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0064] Embodiments of the invention also include a light dispersion / separation module positioned between the flow cell and the fluorescence detector. Light dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices.

[0065] In embodiments of the present disclosure, the subject fluorescence detectors are configured to measure collected light at one or more wavelengths, such as two or more wavelengths, such as five or more different wavelengths, such as ten or more different wavelengths, such as twenty-five or more different wavelengths, such as fifty or more different wavelengths, such as one hundred or more different wavelengths, such as two or more different wavelengths, such as three or more different wavelengths, including measuring light emitted by a sample in a flow stream at over four hundred different wavelengths. In some embodiments, two or more detectors in a flow cytometer as described herein are configured to measure the same or overlapping wavelengths of collected light.

[0066] In some embodiments, the subject's fluorescence detector is configured to measure light collected over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the subject's detector is configured to collect a spectrum of light over a range of wavelengths. For example, the particle analyzer may include one or more detectors configured to collect a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the subject's detector is configured to measure light emitted by a sample in the flow stream at one or more specific wavelengths. For example, the particle analyzer may include one or more detectors configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, one or more detectors may be configured to be paired with a particular fluorophore, such as one used with a sample in a fluorescence assay.

[0067] Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan, 49(pt 1):17-28; Linden, et al., Semin Throm Hemost. 2004 Oct, 30(5):502-11; Alison, et al. J Pathol, 2010 Dec, 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular examples, flow cytometry systems of interest are BD Biosciences FACSCanto™ flow cytometers, BD Biosciences FACSCanto™ II flow cytometers, BD Accuri™ flow cytometers, BD Accuri™ C6 Plus flow cytometers, BD Biosciences FACSCelesta™ flow cytometers, BD Biosciences FACSLyric™ flow cytometers, BD Biosciences FACSVerse™ flow cytometers, BD Biosciences FACSymphony™ flow cytometers, BD Biosciences LSRFortessa™ flow cytometers, BD Biosciences LSRFortessa™ X-20 flow cytometers, BD Biosciences FACSPresto™ flow cytometers, BD Biosciences FACSVia™ flow cytometers, and BD Biosciences FACSCalibur™ cell sorters, BD Biosciences FACSCount™ cell sorters, BD Biosciences FACSLyric™ cell sorters, BD Biosciences These include the Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ fusion cell sorter and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, and others.

[0068] In some embodiments, the system is a flow cytometry system, e.g., a flow cytometry system as described in U.S. Pat. Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469 ... No. 10,481,074, No. 10,302,545, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. No. 9,952,076, No. 9,933,341, No. 9,726,527, No. 9,453,789, No. 9,200,334, No. 9,0 No. 97,640, No. 9,095,494, No. 9,092,034, No. 8,975,595, No. 8,753,573, No. 8,233,1 No. 46, No. 8,140,300, No. 7,544,326, No. 7,201,875, No. 7,129,505, No. 6,821,740, Nos. 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766.

[0069] In certain examples, the flow cytometry system of the present invention may be any of those described in Diebold, et al. Nature Photonics Vol. 7(10), 806-810 (2013), as well as those described in U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,1 No. 11, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0070] In certain embodiments, the system further includes a processor having a memory operatively coupled to the processor, the memory including instructions stored in the memory that, when executed by the processor, cause the processor to cluster the fluorescent flow cytometer data, determine a measure of spillover diffusion, adjust the fluorescent flow cytometer data for spillover diffusion, and partition the spillover diffusion adjusted fluorescent flow cytometer data.

[0071] In some embodiments, the processor is configured to generate one or more population clusters based on determined parameters of the analytes (e.g., cells, particles) in the sample. In embodiments, the fluorescent flow cytometer data includes signals from a plurality of different fluorochromes (e.g., ranging from 2-20 different fluorochromes, including 3-5 different fluorochromes). In some embodiments, the plurality of different fluorochromes includes 2 or more different fluorochromes, including 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, including 20 or more different fluorochromes. Thus, the populations are recognized as clusters in the data. Each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte, although conversely, clusters corresponding to noise or background are also typically observed. Clusters may be defined within a subset of dimensions, for example with respect to a subset of measured fluorescent parameters (i.e., fluorescent dyes) that correspond to populations that differ only within a subset of measured parameters or features extracted from sample measurements.

[0072] In embodiments, the processor includes instructions for performing supervised clustering of the fluorescent flow cytometer data. Thus, in certain embodiments, a population of fluorescent flow cytometer data is clustered based on the status of each data point relative to a hierarchy. In some embodiments, the hierarchy establishes how data points that are positive or negative for the same fluorochrome are grouped together. In some embodiments, clustering the fluorescent flow cytometer data includes comparing the fluorescent flow cytometer data to a threshold. In other words, fluorescent flow cytometer data that is above the threshold may be described as positive for a particular fluorochrome, while fluorescent flow cytometer data that is below the threshold may be described as negative for the same fluorochrome.

[0073] After the flow cytometer data has been clustered, the processor may be configured to determine a measure of spillover diffusion for the population of fluorescent flow cytometer data. In some embodiments, the unadjusted flow cytometer data includes noise (i.e., spillover diffusion) resulting from unintended detection of light of particular wavelengths by one or more detectors. In certain embodiments, determining the measure of spillover diffusion includes quantifying the extent to which the intensity of light collected from a first fluorochrome by a first detector is affected by the simultaneous collection of light from a second fluorochrome by the same detector. In certain embodiments, determining the measure of spillover diffusion includes calculating a spillover diffusion coefficient. In some embodiments, the spillover diffusion coefficient is calculated according to the following formula:

[0074]

number

[0075] In the above formula, SS is the spillover diffusion coefficient, and Δσ f is the incremental standard deviation indicating the spread of emission between the positive and negative fluorescence flow cytometer data collected from the fluorochrome, and Δd is the difference in intensity of fluorescence between the positive and negative fluorescence flow cytometer data received by the fluorescence detector.

[0076] Thus, the spillover diffusion coefficient measures the degree to which the fluorescence flow cytometer data collected by a given fluorescence detector is affected by the presence of light associated with a particular fluorochrome. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed to the fluorescence flow cytometer data by light emitted from the associated fluorochrome being collected by the given detector. In embodiments, a higher spillover diffusion coefficient corresponds to more spillover diffusion for a given fluorochrome-detector pair.

[0077] In some embodiments, determining the measure of spillover diffusion also includes calculating a spillover diffusion coefficient for each possible fluorescent detector-fluorochrome combination so that it can be determined how the fluorescent flow cytometer data collected at each detector is affected by the presence of light associated with each fluorochrome. In embodiments, the calculated spillover diffusion coefficients for each fluorescent detector-fluorochrome pair are combined in a spillover diffusion matrix. In certain embodiments, the spillover diffusion matrix demonstrates how the detection of a particular fluorochrome by its corresponding detector is affected by spillover from other fluorochromes. Cells where columns and rows intersect are filled with the calculated spillover diffusion coefficients for the fluorescent detector-fluorochrome pair that indicate the degree to which the fluorochrome in question contributes error to the associated detector. The total extent to which a fluorochrome causes spillover diffusion can be approximated by summing all the values ​​in its row, and the total extent to which a detector is affected by spillover diffusion can be calculated by summing all the values ​​in its column. In some embodiments, the spillover diffusion coefficients are summed to calculate the total diffusion effect (i.e., the cumulative effect of spillover diffusion on a particular subset of fluorescence flow cytometer data).

[0078] In some embodiments, the spillover diffusion matrix is ​​calculated by an AutoSpread algorithm. The AutoSpread algorithm is configured to create a spillover diffusion matrix (e.g., as described above) without requiring a distinction between a population of flow cytometers that are positive and a population of flow cytometers that are negative for a given fluorochrome. AutoSpread characterizes the diffusion contributed to the detection signal of a first fluorochrome by including a second fluorochrome in the same flow cytometry panel. AutoSpread generates one coefficient for each interaction between the fluorescent detector and the fluorochrome and arranges the coefficients in a matrix similar to the spillover diffusion matrix described above. In embodiments, calculating the spillover diffusion coefficient includes assuming that the intensity of the fluorescence collected by the fluorescent detector for the negative population of the flow cytometer data is 0 and the corresponding standard deviation is an unknown quantity. In some embodiments, the spillover diffusion coefficient is calculated according to the following formula:

[0079]

number

[0080] In the above formula, SS is the spillover diffusion coefficient, and σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, and σ0 2 is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data and d is the intensity of the light collected by the fluorescent detector.

[0081] In some embodiments, the flow cytometer data (σ 2To obtain an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data when the intensity of the fluorescence collected by the fluorescence detector for the negative population of (a) is assumed to be zero, the spillover diffusion coefficient is calculated according to a sequence of linear regressions. The fluorescent flow cytometer data is first sorted into quantiles according to the intensity values ​​detected by the fluorescent detector. The number of quantiles is 256 by default, but is adjusted downward to around 8 to ensure that each quantile has a sufficient number of data points to allow a reliable estimate of the standard deviation. The robust standard deviation of the light emitted from the fluorochrome is then regressed against the square root of the median intensity of the light detected for each quantile. The y-intercept of the ordinary least squares fit is obtained as an estimate of the standard deviation in the negative population of the flow cytometer data when the intensity of the light detected for the negative population is assumed to be zero. The estimate of the standard deviation of the light emitted from the fluorochrome is used to obtain a new zero-adjusted standard deviation. The zero-adjusted standard deviation for the fluorochrome is regressed against the square root of the median fluorescence of each quantile detected by the fluorescent detector. The slope of the ordinary least squares fit (calculated by the above formula) is taken as the spillover diffusion coefficient.

[0082] In some embodiments, the processor is configured to adjust the fluorescent flow cytometer data to account for spillover diffusion. In some embodiments, the flow cytometer data is adjusted to eliminate constructive error from spillover diffusion. In embodiments, adjusting the fluorescent flow cytometer data includes generating a separate spillover diffusion adjusted population. In certain embodiments, generating a separate spillover diffusion adjusted population includes subtracting a magnitude of the spillover diffusion from a relevant population of the flow cytometer data, i.e., counteracting the effect of increased signal due to constructive spillover diffusion error. In certain embodiments, the magnitude of the spillover diffusion is determined from a spillover diffusion matrix. In some embodiments, adjusting the flow cytometer data includes subtracting a total diffusion effect from a relevant portion of the flow cytometer data.

[0083] After the populations of fluorescent flow cytometer data have been adjusted for spillover diffusion (e.g., as described above), the processor may be configured to partition the spillover diffusion-adjusted populations of fluorescent flow cytometer data. The partitions are created to formally distinguish between populations of fluorescent flow cytometer data that are classified differently (e.g., representing different phenotypes). In an embodiment, partitioning the separate spillover diffusion-adjusted populations of flow cytometer data includes calculating a Matthew's correlation coefficient. In an embodiment, potential partitions between the separate spillover diffusion-adjusted populations of fluorescent flow cytometer data are evaluated with respect to each of the thresholds. In such an embodiment, a Matthew's correlation coefficient is calculated for each of the thresholds, thereby evaluating the level of agreement between the thresholds and the partitions. Thus, an embodiment of the present invention includes partitioning the populations such that the Matthew's correlation coefficient is optimized with respect to an associated threshold that distinguishes between positive and negative populations of flow cytometer data with respect to a particular fluorochrome. In other words, partitioning the fluorescent flow cytometer data involves maximizing the degree to which separate populations (i.e., populations exhibiting different combinations of fluorescent parameters) are distinguished from one another as determined by their relationship to an associated threshold (e.g., as quantified by Matthew's correlation coefficient). In an embodiment, the processor repeats this process to determine an optimal partition that distinguishes between positive and negative populations for each relevant parameter of the flow cytometer data. In an embodiment, the Matthew's correlation coefficient is calculated according to the following formula:

[0084]

number

[0085] In the above formula, MCC is Matthew's correlation coefficient, TP represents true positive events, TN represents true negative events, FP represents false positive events, and FN represents false negative events.

[0086] According to the present disclosure, a true positive event is fluorescent flow cytometer data that is evaluated as positive for a particular fluorescent dye by both the threshold and the category, a true negative event is fluorescent flow cytometer data that is evaluated as negative for a particular fluorescent dye by both the threshold and the category, a false positive event is fluorescent flow cytometer data that is positive for a particular fluorescent dye by the category and negative by the threshold, and a false negative event is fluorescent flow cytometer data that is negative for a particular fluorescent dye by the category and positive by the threshold.

[0087] In some embodiments of the present invention, the fluorescence flow cytometer data does not include a signal that is positive for a particular fluorochrome. In other words, no fluorescence emanating from the fluorochrome is detected. In such embodiments, partitioning the separate populations of spillover diffusion adjusted fluorescence flow cytometer data may include calculating a balanced precision for each partition as a surrogate metric for determining the optimal partition. The balanced precision is the average of the precision for determining positive events and the precision for determining negative events, and is calculated according to the following formula:

[0088]

number

[0089] In the above formula, BA is the balanced accuracy, TP represents true positive events, TN represents true negative events, FP represents false positive events, and FN represents false negative events.

[0090] Embodiments of the invention also include classifying the partitioned populations of fluorescent flow cytometer data, i.e., determining the subtype of cells or particles designated by each distinct spillover diffusion adjusted population of flow cytometer data. In embodiments, the classification is determined based on a hierarchy (described above). Thus, a population of fluorescent flow cytometer data that has been partitioned (e.g., as described above) is assigned a classification (i.e., phenotyped) based on the combination of fluorescent parameters it exhibits.

[0091] 8 shows a system 800 for flow cytometry in accordance with an exemplary embodiment of the invention. System 800 includes a flow cytometer 810, a controller / processor 890, and a memory 895. Flow cytometer 810 includes one or more excitation lasers 815a-815c, a focusing lens 820, a flow chamber 825, a forward scatter detector 830, a side scatter detector 835, a fluorescence collection lens 840, one or more beam splitters 845a-845g, one or more bandpass filters 850a-850e, one or more longpass ("LP") filters 855a-855b, and one or more fluorescence detectors 860a-860f.

[0092] The pump lasers 815a-c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the pump lasers 815a-c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 8. The laser beams are first directed through one or more of the beam splitters 845a and 845b. The beam splitter 845a transmits 488 nm light and reflects 633 nm light. The beam splitter 845b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects 488 nm and 633 nm light.

[0093] The laser beam is then directed to a focusing lens 820, which focuses the beam into a portion of the fluid stream where the sample particles are located, in a flow chamber 825. The flow chamber is part of a fluidics system that directs the particles, typically one at a time, in a stream to the focused laser beam for interrogation. The flow chamber may include a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0094] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, and re-emits at a variety of different wavelengths depending on the particle's properties, such as the particle's size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent emissions and diffracted, refracted, reflected, and scattered light may be sent through one or more of beam splitters 845a-845g, bandpass filters 850a-850e, longpass filters 855a-855b, and fluorescence collection lens 840 to one or more of a forward scatter detector 830, a side scatter detector 835, and one or more fluorescence detectors 860a-860f.

[0095] Fluorescence collection lens 840 collects light emitted from the particle-laser beam interaction and sends the light towards one or more beam splitters and filters. Bandpass filters, such as bandpass filters 850a-850e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 850a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on each side of the center of the spectral band, i.e., from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light below a particular wavelength. Longpass filters, such as longpass filters 855a-855b, transmit wavelengths of light above a specified wavelength of light. For example, longpass filter 855a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorescent dye. The filters can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0096] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short pass and long pass. For example, beam splitter 805g is a 620SP beam splitter, meaning that beam splitter 845g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 845a-845g can comprise optical mirrors, such as dichroic mirrors.

[0097] The forward scatter detector 830 is positioned off-axis from the direct beam through the flow cell and configured to detect diffracted light, i.e., excitation light traveling primarily in a forward direction through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 835 is configured to detect light refracted and reflected from the surface and internal structure of the particle, which tends to increase with increasing particle complexity of structure. Fluorescence emission from fluorescent molecules associated with the particle may be detected by one or more fluorescence detectors 860a-860f. The side scatter detector 835 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 830, the side scatter detector 835 and the fluorescence detector may be converted to electronic signals (voltages) by the detectors. This data may provide information about the sample.

[0098] During operation, cytometer operation is controlled by controller / processor 890, and measurement data from the detectors may be stored in memory 895 and processed by controller / processor 890. Although not explicitly shown, controller / processor 890 may be coupled to detectors to receive output signals therefrom, and may also be coupled to electrical and electromechanical components of flow cytometer 800 to control lasers, fluid flow parameters, and the like. Input / output (I / O) functionality 897 may also be provided within the system. Memory 895, controller / processor 890, and I / O 897 may be provided entirely as an integral part of flow cytometer 810. In such an embodiment, a display may also form part of I / O functionality 897 for presenting experimental data to a user of cytometer 800. Alternatively, memory 895 and controller / processor 890 and some or all of the I / O functionality may be part of one or more external devices, such as a general purpose computer. In some embodiments, memory 895 and some or all of controller / processor 890 may be in wireless or wired communication with cytometer 810. The controller / processor 890, in conjunction with memory 895 and I / O 897, can be configured to perform various functions associated with the preparation and analysis of flow cytometer experiments.

[0099] The system shown in FIG. 8 includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for a given detector), defined by the configuration of filters and / or splitters in the beam path from the flow cell 825 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels and their associated fluorescent emission bands used for an experiment may be selected to generally match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescent emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, it is the case that generally a portion of the emission spectrum of that label will also overlap with the filter windows of one or more other detectors. This may be referred to as spillover. The I / O 897 may be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations having multiple markers, each cell population having a subset of the multiple markers. I / O 897 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data (e.g., label spectral characteristics and flow cytometer configuration data) may also be stored in memory 895. Controller / processor 890 may be configured to evaluate one or more assignments of labels to markers.

[0100] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 8, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0101] FIG. 9 illustrates a functional block diagram of an example of a processor 900 for analyzing and displaying data. The processor 900 may be configured to implement various processes for controlling the graphical display of a biological event. A flow cytometer 902 may be configured to acquire fluorescent flow cytometer data by analyzing a biological sample (e.g., as described above). The device may be configured to provide the biological event data to the processor 900. A data communication channel may be included between the flow cytometer 902 and the processor 900. The data may be provided to the processor 900 via the data communication channel. The processor 900 may be configured to provide a graphical display including a plot (e.g., as described above) to the display 906. The processor 900 may be further configured to render a gate around a population of the fluorescent flow cytometer data shown by the display device 906, e.g., overlaid on the plot. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest depicted on a single parameter histogram or bivariate plot. In some embodiments, the display may be used to display analyte parameters or saturation detector data.

[0102] The processor 900 may further be configured to display the fluorescent flow cytometer data within the gate on the display device 906 differently from other events within the fluorescent flow cytometer data outside the gate. For example, the processor 900 may be configured to render the color of the fluorescent flow cytometer data contained within the gate differently from the color of the fluorescent flow cytometer data outside the gate. In this manner, the processor 900 may be configured to render a different color to represent each unique population of data. The display device 906 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0103] The processor 900 can be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device can be implemented as a mouse 910. The mouse 910 can initiate a gate selection signal to the processor 900 to identify a population to be displayed or manipulated via the display device 906 (e.g., by clicking on or within the desired gate when a cursor is positioned there). In some implementations, the first device can be implemented as a keyboard 908 or other means for providing an input signal to the processor 900, such as a touch screen, a stylus, a photodetector, or a voice recognition system. Some input devices can include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 9, the mouse 910 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0104] The trigger event can cause the processor 900 to change the way the fluorescent flow cytometer data is displayed, which portions of the data are actually displayed on the display device 906, and / or provide input to further processing, such as selection of a subject population for analysis.

[0105] In some embodiments, the processor 900 can be configured to detect when a gate selection is initiated by the mouse 910. The processor 900 can be further configured to automatically modify the plot visualization to facilitate the gating process. The modification can be based on a particular distribution of the data received by the processor 900.

[0106] The processor 900 may be connected to a storage device 904. The storage device 904 may be configured to receive and store data from the processor 900. The storage device 904 may be further configured to enable retrieval of data, such as fluorescent flow cytometer data, by the processor 900.

[0107] The display device 906 may be configured to receive display data from the processor 900. The display data may include plots of the fluorescent flow cytometer data and gates that delineate sections of the plots. The display device 906 may be further configured to modify the information presented according to input received from the processor 900 in conjunction with input from the apparatus 902, the storage device 904, the keyboard 908, and / or the mouse 910.

[0108] In some implementations, the processor 900 can generate a user interface for receiving the exemplary events for sorting. For example, the user interface can include a control for receiving the exemplary events or exemplary images. The exemplary events or images or exemplary gates can be provided prior to collection of event data for the sample or based on an initial set of events for a portion of the sample.

[0109] Computer Control System Aspects of the present disclosure further include a computer control system, the system further comprising one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program, when loaded into the computer, includes instructions for clustering the fluorescent flow cytometer data into populations according to one or more different parameters (i.e., fluorescent dyes), determining the spillover diffusion between each detector-parameter pair (i.e., by calculating the spillover diffusion coefficient), creating a spillover diffusion matrix showing how the detection of a particular parameter by its corresponding detector is affected by spillover from other parameters, modifying the fluorescent flow cytometer data to compensate for the spillover diffusion by subtracting the magnitude of the spillover diffusion determined by the spillover diffusion matrix, evaluating the quality of the different partitions separating the distinct populations of the fluorescent flow cytometer data by calculating the Matthew correlation coefficient against a threshold value that distinguishes between populations that are positive for a given parameter and populations that are negative for a given parameter, and classifying (i.e., phenotyping) the adjusted populations of the fluorescent flow cytometer data.

[0110] In embodiments, the system is configured to analyze the data within software or analysis tools for analyzing flow cytometer data or nucleic acid sequence data, such as FlowJo® (Ashland, OR). FlowJo® is a software package developed by FlowJo LLC (a subsidiary of Becton Dickinson) for analyzing flow cytometer data. The software is configured to manage and generate graphical reports regarding the flow cytometer data (https: / / www(dot)flowjo(dot)com / learn / flowjo-university / flowjo). The initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®) by appropriate means, such as manual gating, cluster analysis, or other computer techniques. The system, or portions thereof, can be implemented as a software component of software for analyzing data, such as FlowJo®. In these embodiments, a computerized system according to the present disclosure can function as a software "plug-in" for an existing software package, such as FlowJo®.

[0111] In an embodiment, the system includes an input module, a processing module, and an output module. The subject systems may include both hardware and software components, and the hardware components may take the form of one or more platforms, e.g., servers, such that the functional elements, i.e., elements of the system that perform a particular task of the system (managing input and output of information, processing information, etc.), may be performed by executing software applications on and across one or more computer platforms represented by the system.

[0112] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor that accesses a memory having stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that allow a user to manually align the light source with the flow stream based on the first and second light signals, hi some embodiments, the processor includes analog electronics that provide feedback control, such as, for example, negative feedback control.

[0113] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write compact disk, a flash memory device, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a disk drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, a magnetic tape, a removable hard disk, or a floppy disk, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in conjunction with the memory storage devices.

[0114] In some embodiments, a computer program product is described comprising a computer usable medium having stored thereon control logic (computer software program including program code). The control logic, when executed by a processor, causes the computer to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example using hardware state machines. The implementation of hardware state machines to perform the functions described herein will be apparent to one skilled in the relevant art.

[0115] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor via a communication channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices together with the memory. For example, a magnetic disk or an optical disk may carry the programming and be read by a disk writer / reader. The system of the present invention also includes programming, for example in the form of a computer program product, an algorithm for use in implementing the above-described method. The programming according to the present invention may be recorded on a computer-readable medium, for example, any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0116] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a cellular phone (i.e., a smartphone).

[0117] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), ZigBee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0118] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment) configured for similar complementary data communications.

[0119] In one embodiment, the communications interface is configured for infrared communications, Bluetooth® communications, or any other suitable wireless communications protocol to enable the subject system to communicate with other devices, such as a computer terminal and / or network, a communications-enabled mobile phone, a personal digital assistant, or any other communications device that a user may use in conjunction with.

[0120] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing the Internet Protocol (IP) via a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0121] In one embodiment, the subject system is configured to communicate wirelessly with the server device via a communication interface using common standards such as, for example, 802.11 or Bluetooth® RF protocols, or IrDA infrared protocols. The server device may be another portable device, such as a smartphone, a personal digital assistant (PDA) or a notebook computer, or a larger device, such as a desktop computer, an appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0122] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored in the subject system, e.g., in any data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0123] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite network, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system are typically of a class of computers commonly referred to as servers, but may be any type of known or future developed computer platform. However, they may be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication systems, including networked or non-networked wireless systems. They may be co-located or physically separated.In some cases, various operating systems may be used on any of the computer platforms, depending on the type and / or configuration of the computer platform selected. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, etc.

[0124] FIG. 10 illustrates a general architecture of an exemplary computing device 1000 according to certain embodiments. The general architecture of the computing device 1000 illustrated in FIG. 10 includes an arrangement of computer hardware and software components. However, it is not necessary to illustrate all of these generally conventional elements to provide an enabling disclosure. As illustrated, the computing device 1000 includes a processing unit 1010, a network interface 1020, a computer-readable medium drive 1030, an input / output device interface 1040, a display 1050, and an input device 1060, all of which may communicate with each other by a communication bus. The network interface 1020 may provide connectivity to one or more networks or computing systems. Thus, the processing unit 1010 may receive information and instructions from other computing systems or services over a network. The processing unit 1010 may also communicate with a memory 1070 and further provide output information to an optional display 1050 via the input / output device interface 1040. For example, analysis software (e.g., data analysis software or programs such as FlowJo®) stored as executable instructions in non-transitory memory of the analysis system can display flow cytometry event data to a user. The input / output device interface 1040 can also accept input from optional input devices 1060, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input device.

[0125] The memory 1070 may include computer program instructions (grouped as modules or components in some embodiments) that the processing unit 1010 executes to implement one or more embodiments. The memory 1070 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. The memory 1070 may store an operating system 1072 that provides computer program instructions used by the processing unit 1010 in the general management and operation of the computing device 1000. Data may be stored in the data storage device 1090. The memory 1070 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0126] Computer-readable storage medium Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for implementing the subject methods. The computer-readable storage medium may be employed on one or more computers for full or partial automation of a system for practicing the methods described herein. In some embodiments, instructions according to the methods described herein may be coded on a computer-readable medium in the form of "programming", and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), whether such devices are internal or external to the computer. In some examples, the instructions may be provided on an integrated circuit device. The target integrated circuit device may include a reconfigurable field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a complex programmable logic device (CPLD) in certain examples. A file containing information can be "stored" on a computer-readable medium, where "storing" means recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein can be performed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java (Sun Microsystems, Inc., Santa Clara, CA), Visual Basic (Microsoft Corp., Redmond, WA), and C++ (AT&T Corp., Bedminster, NJ), as well as any of many other languages.

[0127] In some embodiments, a subject computer readable storage medium includes a computer program stored thereon, the computer program including instructions, when loaded into a computer, for clustering the fluorescent flow cytometer data into populations according to one or more different parameters; determining the spillover diffusion between each detector-parameter pair (i.e., by calculating a spillover diffusion coefficient); creating a spillover diffusion matrix indicating how detection of a particular parameter by its corresponding detector is affected by spillover from other parameters; modifying the fluorescent flow cytometer data to compensate for the spillover diffusion by subtracting a magnitude of the spillover diffusion determined by the spillover diffusion matrix; assessing the quality of different partitions separating distinct populations of the fluorescent flow cytometer data by calculating a Matthew correlation coefficient against a threshold value that distinguishes between populations that are positive for a given parameter and populations that are negative for a given parameter; and classifying (i.e., phenotyping) the adjusted populations of the fluorescent flow cytometer data.

[0128] In embodiments, the system is configured to analyze the data within software or analysis tools for analyzing flow cytometer data or nucleic acid sequence data, such as FlowJo®. The initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®) by appropriate means, such as manual gating, cluster analysis, or other computer techniques. The system, or portions thereof, can be implemented as a software component of software for analyzing data, such as FlowJo®. In these embodiments, a computerized system according to the present disclosure can function as a software "plug-in" for existing software packages, such as FlowJo®.

[0129] The computer readable storage medium can be used on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs that may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0130] Utilities The devices, methods, and computer systems of the present invention find use in a variety of applications where it is desirable to increase the resolution and accuracy in determining parameters of analytes (e.g., cells, particles) in biological samples. For example, the present disclosure finds use in analyzing data affected by spillover diffusion. Because flow cytometry often involves the collection of multiple fluorescence parameters by multiple detectors, the detected fluorescence intensity may be erroneously inflated due to the same light being detected by multiple detectors. Thus, the present disclosure finds use during the analysis of flow cytometer data that includes signals from multiple fluorochromes. The subject devices, methods, and computer systems also find use in classifying (i.e., phenotyping) populations in flow cytometer data that are typically mischaracterized due to the effects of spillover diffusion. In some embodiments, the subject methods and systems provide fully automated protocols, such that little, if any, adjustments to the data require human input.

[0131] The present disclosure can be used to characterize many types of analytes, particularly those relevant to medical diagnostics or protocols for patient care, including, but not limited to, proteins (including both free proteins, as well as proteins and proteins bound to the surfaces of structures such as cells), nucleic acids, viral particles, etc. Additionally, samples can be from in vitro or in vivo sources, and samples can be diagnostic samples.

[0132] kit Aspects of the present disclosure further include kits, which include storage media such as floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid state disks, and network attached storage (NAS). Any of these program storage media, or others now in use or that may later be developed, may be included in the kits. In embodiments, the program storage media includes instructions for clustering fluorescent flow cytometer data into populations, determining spillover spread of the populations, adjusting the flow cytometer data based on the spillover spread, and determining partitions between the adjusted flow cytometer data (e.g., as described above). In embodiments, the instructions included in the computer readable media provided in the subject kits or portions thereof may be implemented as software components of software for analyzing data, such as FlowJo®. In these embodiments, a computer control system according to the present disclosure may function as a software "plug-in" for existing software packages, such as FlowJo®.

[0133] In addition to the above components, the subject kits may further include (in some embodiments) instructions for installing a plug-in into an existing software package, such as FlowJo®. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as a sheet or sheets of paper on which the information is printed, in the kit packaging, in a package insert, etc. Yet another form in which these instructions may be present is a computer readable medium on which the information is recorded, such as a diskette, a compact disc (CD), a portable flash drive, etc. Yet another form in which these instructions may be present is a website address that may be used via the Internet to access the information at a remote site.

[0134] Notwithstanding the appended claims, the present disclosure is also defined by the following appended claims. 1. A method for classifying fluorescence flow cytometer data, comprising: processing the fluorescence flow cytometer data using a supervised algorithm; The supervised algorithm is Clustering the fluorescent flow cytometer data into populations; Determining a measure of spillover diffusion for a population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; Establishing a partition between the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data; The method is configured to: 2. The method of claim 1, wherein the fluorescent flow cytometer data is clustered into populations based on positivity or negativity of the fluorescent flow cytometer data for a particular fluorescent dye. 3. The method of claim 1 or 2, wherein the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorescent dye based on a relationship of the fluorescent flow cytometer data to a threshold value. 4. The method of any one of claims 1 to 3, wherein determining spillover diffusion comprises quantifying the extent to which fluorescence flow cytometer data collected by a fluorescence detector is increased by collection of light emitted from a particular fluorescent dye. 5. The method of any one of claims 1 to 4, wherein determining the spillover diffusion comprises calculating a spillover diffusion coefficient of the fluorescence detector-fluorochrome pair.

[0135] 6. The spillover diffusion coefficient is calculated according to the following formula:

[0136]

number

[0137] During the ceremony, SS is the spillover diffusion coefficient, Δσ f is the incremental standard deviation indicating the spread in emission between the positive and negative fluorescence flow cytometer data collected from a fluorochrome, The method described in Appendix 5, wherein Δd is the difference in fluorescence intensity between the positive fluorescence flow cytometer data and the negative fluorescence flow cytometer data received by the fluorescence detector. 7. The method of claim 5, wherein calculating the spillover diffusion coefficient includes assuming that the intensity of fluorescence collected by the fluorescence detector for a negative population of the flow cytometer data is 0. 8. The spillover diffusion coefficient is calculated according to the following formula:

[0138]

number

[0139] During the ceremony, SS is the spillover diffusion coefficient, σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, σ0 2 is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, The method described in Appendix 7, wherein d is the intensity of light collected by the fluorescence detector. 9. σ0 2 is calculated by linear regression. 10. The method of any one of claims 1 to 9, wherein the fluorescent flow cytometer data is collected from light emitted from a number of different fluorescent dyes.

[0140] 11. The method of claim 10, wherein the plurality of different fluorescent dyes ranges from 2 to 20 different fluorescent dyes. 12. The method of claim 10 or 11, wherein the plurality of different fluorescent dyes is in the range of 3 to 5 different fluorescent dyes. 13. The method of any one of claims 10 to 12, wherein the spillover diffusion coefficient is calculated for each fluorescence detector-fluorescent dye pair. 14. The method of any one of appendices 10 to 13, wherein the spillover diffusion coefficients calculated for each fluorescence detector-fluorescent dye pair are combined in a spillover diffusion matrix. 15. The method of claim 14, further comprising calculating a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes based on the spillover diffusion matrix.

[0141] 16. The method of claim 15, wherein adjusting the fluorescent flow cytometer data includes subtracting the magnitude of spillover diffusion for each of a plurality of different fluorescent dyes from the population of fluorescent flow cytometer data corresponding to that fluorescent dye. 17. The method of any one of claims 1 to 16, wherein establishing a partition between distinct spillover diffusion-adjusted populations of the fluorescent flow cytometer data comprises evaluating the separation of the distinct spillover diffusion-adjusted populations against a threshold value. 18. The method of claim 17, wherein assessing separation of the distinct spillover diffusion adjusted populations against a threshold value comprises calculating a Matthew correlation coefficient. 19. The method of claim 18, wherein the fluorescent flow cytometer data does not include positive data for at least one fluorescent dye. 20. The method of claim 19, wherein evaluating the separation of the separate spillover diffusion adjusted populations against a threshold value includes calculating a balanced accuracy.

[0142] 21. The method of any one of claims 1 to 20, wherein the separate spillover diffusion adjusted populations of fluorescence flow cytometer data are partitioned according to a hierarchy. 22. The method of claim 21, wherein the hierarchy specifies an association between spillover diffusion adjusted populations of fluorescent flow cytometer data that are positive or negative for a particular fluorescent dye and a corresponding phenotype.

[0143] 23. A system comprising: a particle analyzer component configured to acquire fluorescence flow cytometer data; 1. A processor comprising a memory operatively coupled to the processor, the memory including instructions stored in the memory, the instructions, when executed by the processor, causing the processor to: Clustering the fluorescent flow cytometer data into populations; Determining a measure of spillover diffusion for a population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; Establishing a partition between the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data; A processor and Including, the system. 24. The system of claim 23, wherein the fluorescent flow cytometer data is clustered into populations based on positivity or negativity of the fluorescent flow cytometer data for a particular fluorescent dye. 25. The system of claim 23 or 24, wherein the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorescent dye based on a relationship of the fluorescent flow cytometer data to a threshold value. 26. A system described in any one of appendices 23 to 25, wherein determining spillover diffusion includes quantifying the extent to which fluorescent flow cytometer data collected by a fluorescent detector is increased by collection of light emitted from a particular fluorescent dye. 27. A system according to any one of claims 23 to 26, wherein determining the spillover diffusion includes calculating a spillover diffusion coefficient of a fluorescence detector-fluorescent dye pair.

[0144] 28. The spillover diffusion coefficient is calculated according to the following formula:

[0145]

number

[0146] During the ceremony, SS is the spillover diffusion coefficient, Δσ f is the incremental standard deviation indicating the spread in emission between the positive and negative fluorescence flow cytometer data collected from a fluorochrome, 28. The system of claim 27, wherein Δd is the difference in fluorescence intensity between the positive fluorescence flow cytometer data and the negative fluorescence flow cytometer data received by the fluorescence detector. 29. The system of claim 27, wherein calculating the spillover diffusion coefficient includes assuming that the intensity of fluorescence collected by the fluorescence detector for a negative population of the flow cytometer data is 0. 30. The spillover diffusion coefficient is calculated according to the following formula:

[0147]

number

[0148] During the ceremony, SS is the spillover diffusion coefficient, σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, σ0 2 is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, 30. The system of claim 29, wherein d is the intensity of light collected by the fluorescence detector. 31. σ0 2 31. The system of claim 30, wherein: 32. A system according to any one of claims 23 to 31, wherein the fluorescent flow cytometer data is collected from light emitted from a number of different fluorescent dyes.

[0149] 33. The system of claim 32, wherein the plurality of different fluorescent dyes ranges from 2 to 20 different fluorescent dyes. 34. The system of claim 32 or 33, wherein the plurality of different fluorescent dyes is in the range of 3 to 5 different fluorescent dyes. 35. A system according to any one of claims 32 to 34, wherein a spillover diffusion coefficient is calculated for each fluorescence detector-fluorescent dye pair. 36. A system according to any one of appendices 32 to 35, wherein the spillover diffusion coefficients calculated for each fluorescence detector-fluorescent dye pair are combined in a spillover diffusion matrix. 37. The system of claim 36, further comprising calculating a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes based on the spillover diffusion matrix.

[0150] 38. The system of claim 37, wherein adjusting the fluorescent flow cytometer data includes subtracting a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes from the population of fluorescent flow cytometer data corresponding to that fluorescent dye. 39. A system described in any one of appendices 23 to 38, wherein establishing a partition between separate spillover diffusion adjusted populations of the fluorescent flow cytometer data includes evaluating the separation of the separate spillover diffusion adjusted populations against a threshold value. 40. The system of claim 39, wherein assessing separation of the separate spillover diffusion adjusted populations against a threshold value includes calculating a Matthew correlation coefficient. 41. The system of claim 40, wherein the fluorescent flow cytometer data does not include positive data for at least one fluorescent dye. 42. The system of claim 41, wherein evaluating the separation of the separate spillover diffusion adjusted populations against a threshold value includes calculating a balanced accuracy.

[0151] 43. A system according to any one of appendices 23 to 42, wherein the separate spillover diffusion adjusted populations of the fluorescence flow cytometer data are partitioned according to a hierarchy. 44. The system of claim 43, wherein the hierarchy specifies an association between spillover diffusion adjusted populations of fluorescent flow cytometer data that are positive or negative for a particular fluorescent dye and a corresponding phenotype.

[0152] 45. A non-transitory computer readable storage medium having stored thereon instructions for classifying flow cytometer data according to a method, comprising: The method includes processing flow cytometer data with a supervised algorithm; The supervised algorithm is Clustering the fluorescent flow cytometer data into populations; Determining a measure of spillover diffusion for a population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; Establishing a partition between the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of the fluorescent flow cytometer data; A non-transitory computer-readable storage medium configured to: 46. ​​The non-transitory computer-readable storage medium of claim 45, wherein the fluorescent flow cytometer data is clustered into populations based on positivity or negativity of the fluorescent flow cytometer data for particular fluorescent dyes. 47. The non-transitory computer-readable storage medium of claim 45 or 46, wherein the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorescent dye based on a relationship of the fluorescent flow cytometer data to a threshold value. 48. A non-transitory computer-readable storage medium according to any one of claims 45 to 47, wherein determining spillover diffusion includes quantifying the degree to which fluorescent flow cytometer data collected by a fluorescent detector is increased by the collection of light emitted from a particular fluorescent dye. 49. A non-transitory computer-readable storage medium according to any one of claims 45 to 48, wherein determining the spillover diffusion includes calculating a spillover diffusion coefficient of a fluorescence detector-fluorescent dye pair.

[0153] 50. The spillover diffusion coefficient is calculated according to the following formula:

[0154]

number

[0155] During the ceremony, SS is the spillover diffusion coefficient, Δσ f is the incremental standard deviation indicating the spread in emission between the positive and negative fluorescence flow cytometer data collected from a fluorochrome, 50. The non-transitory computer-readable storage medium of claim 49, wherein Δd is the difference in fluorescence intensity between the positive fluorescence flow cytometer data and the negative fluorescence flow cytometer data received by the fluorescence detector. 51. The non-transitory computer-readable storage medium of claim 49, wherein calculating the spillover diffusion coefficient includes assuming that the intensity of fluorescence collected by the fluorescence detector for a negative population of the flow cytometer data is 0. 52. The spillover diffusion coefficient is calculated according to the following formula:

[0156]

number

[0157] During the ceremony, SS is the spillover diffusion coefficient, σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, σ0 2 is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, 52. The non-transitory computer-readable storage medium of claim 51, wherein d is the intensity of light collected by the fluorescence detector. 53. σ0 2 53. The non-transitory computer-readable storage medium of claim 52, wherein: 54. A non-transitory computer-readable storage medium according to any one of appendices 45 to 53, wherein the fluorescent flow cytometer data is collected from light emitted from a number of different fluorescent dyes.

[0158] 55. The non-transitory computer-readable storage medium of claim 54, wherein the plurality of different fluorescent dyes is in the range of 2 to 20 different fluorescent dyes. 56. The non-transitory computer-readable storage medium of claim 54 or 55, wherein the plurality of different fluorescent dyes is in the range of 3 to 5 different fluorescent dyes. 57. A non-transitory computer-readable storage medium according to any one of claims 54 to 56, wherein a spillover diffusion coefficient is calculated for each fluorescence detector-fluorescent dye pair. 58. A non-transitory computer-readable storage medium according to any one of claims 54 to 57, wherein the spillover diffusion coefficients calculated for each fluorescence detector-fluorescent dye pair are combined in a spillover diffusion matrix. 59. The non-transitory computer-readable storage medium of claim 58, further comprising calculating a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes based on the spillover diffusion matrix.

[0159] 60. The non-transitory computer-readable storage medium of claim 59, wherein adjusting the fluorescent flow cytometer data includes subtracting a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes from the population of fluorescent flow cytometer data corresponding to that fluorescent dye. 61. A non-transitory computer-readable storage medium according to any one of claims 45 to 60, wherein establishing a partition between separate spillover diffusion adjusted populations of the fluorescent flow cytometer data includes evaluating the separation of the separate spillover diffusion adjusted populations against a threshold value. 62. The non-transitory computer-readable storage medium of claim 61, wherein assessing separation of the separate spillover diffusion adjusted populations against a threshold value includes calculating a Matthew correlation coefficient. 63. The non-transitory computer-readable storage medium of claim 62, wherein the fluorescent flow cytometer data does not include positive data for at least one fluorescent dye. 64. The non-transitory computer-readable storage medium of claim 63, wherein evaluating the separation of the separate spillover diffusion adjusted populations against a threshold value includes calculating a balanced accuracy.

[0160] 65. A non-transitory computer-readable storage medium according to any one of appendixes 45 to 64, wherein the separate spillover diffusion adjusted populations of fluorescent flow cytometer data are partitioned according to a hierarchy. 66. The non-transitory computer-readable storage medium of claim 65, wherein the hierarchy specifies an association between spillover diffusion adjusted populations of fluorescent flow cytometer data that are positive or negative for a particular fluorescent dye and a corresponding phenotype.

[0161] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of this invention that certain changes and modifications can be made in the practice of the invention without departing from the spirit or scope of the appended claims.

[0162] Thus, the above merely describes the principles of the invention. It is understood that those skilled in the art may devise various modifications that embody the principles of the invention and are within the spirit and scope of the invention, although not expressly described or shown herein. Furthermore, all examples and conditional terms recited herein are intended primarily to aid the reader in understanding the principles of the invention and the concepts provided by the inventors to further the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein that recite principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Moreover, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed to perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be a public dedication, regardless of whether such disclosure is expressly recited in the claims.

[0163] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) are expressly defined as invoking a limitation in a claim only when a phrase that is exactly the same as "means for" or "step for" is recited at the beginning of the limitation in the claim, and if such a phrase that is exactly the same is not used in the limitation in the claim, neither 35 U.S.C. 112(f) nor 35 U.S.C. 112(6) shall apply.

[0164] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. §119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 62 / 968,516, filed January 31, 2019, and to the filing date of U.S. Provisional Patent Application No. 63 / 053,108, filed July 17, 2019, the disclosures of which are incorporated herein by reference.

Claims

1. 1. A method for classifying fluorescence flow cytometer data, comprising: processing the fluorescence flow cytometer data using a supervised algorithm; The supervised algorithm comprises: clustering the fluorescence flow cytometer data into populations; determining a measure of spillover diffusion for the population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; establishing partitions between the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data; The method is configured to:

2. The method of claim 1 , wherein the fluorescent flow cytometer data is clustered into populations based on positivity or negativity of the fluorescent flow cytometer data for particular fluorochromes.

3. The method of claim 1 or 2, wherein the fluorescent flow cytometer data is determined to be positive or negative for a particular fluorescent dye based on a relationship of the fluorescent flow cytometer data to a threshold value.

4. 4. The method of claim 1, wherein determining spillover diffusion comprises quantifying the extent to which fluorescence flow cytometer data collected by a fluorescence detector is increased by collection of light emitted from a particular fluorescent dye.

5. The method of any one of claims 1 to 4, wherein determining the spillover diffusion comprises calculating a spillover diffusion coefficient of a fluorescence detector-fluorochrome pair.

6. The spillover diffusion coefficient is calculated according to the following formula: [0010] During the ceremony, SS is the spillover diffusion coefficient, Δσ ｆ is the incremental standard deviation indicating the spread in emission between the positive and negative fluorescence flow cytometer data collected from a fluorochrome, Δd is the difference in fluorescence intensity between the positive and negative fluorescence flow cytometer data received by the fluorescence detector; The method according to claim 5.

7. 6. The method of claim 5, wherein calculating the spillover diffusion coefficient comprises assuming that the intensity of the fluorescence collected by the fluorescence detector for a negative population of flow cytometer data is zero.

8. The spillover diffusion coefficient is calculated according to the following formula: [0025] During the ceremony, SS is the spillover diffusion coefficient, σ ２ is the standard deviation of the positive population of the fluorescence flow cytometer data, σ ０ ２ is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, d is the intensity of the light collected by the fluorescence detector; The method of claim 7.

9. σ ０ ２ The method of claim 8 , wherein is calculated by linear regression.

10. The method of any one of claims 1 to 9, wherein the fluorescence flow cytometer data is collected from light emitted from a number of different fluorescent dyes.

11. The method of claim 10, wherein the spillover diffusion coefficients calculated for each fluorescence detector-fluorochrome pair are combined in a spillover diffusion matrix.

12. The method of claim 1 , further comprising calculating a magnitude of spillover diffusion for each of a plurality of different fluorescent dyes based on the spillover diffusion matrix.

13. 13. The method of claim 12, wherein adjusting the fluorescent flow cytometer data comprises subtracting the magnitude of spillover diffusion for each of the plurality of different fluorescent dyes from a population of fluorescent flow cytometer data corresponding to that fluorescent dye.

14. 1. A system comprising: a particle analyzer component configured to acquire fluorescence flow cytometer data; 11. A processor comprising: a memory operatively coupled to the processor, the memory including instructions stored in the memory, the instructions, when executed by the processor, causing the processor to: clustering the fluorescence flow cytometer data into populations; determining a measure of spillover diffusion for the population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; establishing partitions between the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data; A processor and Including, the system.

15. A non-transitory computer readable storage medium having stored thereon instructions for classifying fluorescent flow cytometer data according to a method, comprising: The method includes processing the fluorescence flow cytometer data using a supervised algorithm; The supervised algorithm comprises: clustering the fluorescence flow cytometer data into populations; determining a measure of spillover diffusion for the population of fluorescence flow cytometer data; adjusting the population of fluorescent flow cytometer data based on the determined spillover diffusion to generate a separate spillover diffusion adjusted population; establishing partitions between the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data to classify the distinct spillover diffusion adjusted populations of fluorescent flow cytometer data; 4. The method of claim 3, A non-transitory computer-readable storage medium.

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