Optimal scaling method and system for cytometric data for machine learning analysis

Abstract

Aspects of the present disclosure include methods for processing and scaling cytometric data. The method, according to certain embodiments, includes acquiring cytometric data of a sample, the data including measurements of multiple parameters from illuminated particles in the sample flowing in a flow stream; identifying a parameter of interest; specifying positive and negative measurement intervals for the parameter of interest; and scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. Systems for practicing the subject methods are also provided. Non-transitory computer-readable storage media are also described.

Description

[Background technology]

[0001] Flow-type particle detection and analysis systems, such as flow cytometers, are used to detect, analyze, and sometimes sort particles in a fluid sample based on at least one measured property of the particle. Visualization of data obtained from flow-type particle detection and analysis systems is an important part of the analysis and characterization of collected data, and is used, for example, in biological and medical research.

[0002] Analysis of data obtained from a flow-type particle detection system may involve visualization of the data, such as displaying a plot of data obtained from several different detector channels of the particle detection system, where one or more parameters displayed on the plot have been scaled. Analysis of the data, such as analysis of a visual representation of the data using one or more scaled parameters, may be found to facilitate understanding and characterization of particles exposed to the particle detection system, and importantly, may be used to understand and characterize populations or clusters of particles.

[0003] Analysis of cytometric data using one or more scaled parameters can play an important role in understanding data populations by distinguishing between signals that indicate similarities or differences between particles, such as cell types, and noise due to, for example, measurement or instrument error. The role of scaling parameters in distinguishing between signal and noise is even more pronounced when the cytometric data is high-dimensional, because high-dimensional data provides additional opportunities for noise to influence how particles cluster. Appropriate scaling of cytometric data, especially high-dimensional data, can improve signal-to-noise characteristics in analyses by mitigating or compressing noise within the cytometric data. Summary of the Invention

[0004] Embodiments of the present invention introduce new techniques for more effectively scaling cytometric data, particularly in terms of improving the signal-to-noise ratio of the cytometric data, thereby improving the usefulness of flow-type particle detection and analysis systems.

[0005] Aspects of the present disclosure include methods for scaling cytometric data. The method, according to certain embodiments, includes acquiring cytometric data of a sample, the cytometric data including measurements of multiple parameters from illuminated particles in the sample flowing in a flow stream, identifying a parameter of interest, specifying positive and negative measurement intervals for the parameter of interest, and scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0006] In some embodiments, transforming the parameter of interest includes rescaling a specified negative measurement interval of the parameter of interest. In such embodiments, rescaling the specified negative measurement interval of the parameter of interest may include reducing the standard deviation of the specified negative measurement interval of the parameter of interest. In other embodiments, transforming the parameter of interest further includes rescaling a specified positive measurement interval of the parameter of interest. In such embodiments, rescaling the specified positive measurement interval may include rescaling the positive measurement interval to a predetermined size. In some cases, the predetermined size is the size of the scaled positive measurement interval corresponding to a second parameter of the plurality of parameters.

[0007] In embodiments of the subject method, transforming the parameter of interest includes adaptively scaling the parameter of interest according to:

[0008]

number

[0009] where s(x) represents the adaptively scaled measure of the parameter of interest, x represents the unscaled measure of the parameter of interest, and (n - ,n + ) is the specified negative measurement interval of the parameter of interest, and (n + , p) is the specified positive measurement interval of the parameter of interest, c is the compression ratio, X is the median of the negative measurement interval, and SD is the standard deviation of the negative measurement interval, calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval;

[0010]

number

[0011] z(x) is the z-transform according to

[0012]

number

[0013] g(z) is the inverse hyperbolic sine function according to

[0014]

number

[0015]

number

[0016] is μ=z(n + ) and σ=1. In such an embodiment, the default value of the compression ratio c may be 70.

[0017] In embodiments, the subject methods further include displaying the scaled cytometric data. In some cases, displaying the scaled cytometric data includes displaying a plot of the cytometric data including the transformed parameter of interest.

[0018] In some embodiments, designating at least one of a positive and negative measurement interval for the parameter of interest comprises performing one-dimensional gating to designate the measurement interval. In other embodiments, designating at least one of a positive and negative measurement interval for the parameter of interest comprises applying a fluorescence minus one control to designate the measurement interval. In yet other embodiments, designating at least one of a positive and negative measurement interval for the parameter of interest comprises applying a mathematical model to designate the measurement interval. In some cases, designating one or both of a positive and negative measurement interval for the parameter of interest comprises applying a machine learning algorithm to designate the measurement interval.

[0019] Embodiments of the subject method may further include identifying one or more additional parameters of interest, specifying positive and negative measurement intervals for each additional parameter of interest, and scaling the cytometric data by transforming each additional parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In such embodiments, the specified positive measurement intervals for each parameter of interest may be rescaled to the same predetermined size.

[0020] Other embodiments of the subject methods may further include clustering the scaled cytometric data by applying a clustering algorithm to the scaled cytometric data, hi such embodiments, displaying the scaled cytometric data may include displaying clusters of the scaled cytometric data.

[0021] In some embodiments, the scaled cytometric data is used to improve the performance of clustering algorithms applied to the cytometric data, while in other embodiments, the scaled cytometric data is used to reduce the effects of measurement noise.

[0022] In some cases, the particles are cells. In such cases, the scaled cytometric data can be used to distinguish between two similar cell populations. In embodiments of the subject methods, the cytometric data is high-dimensional data. In such embodiments, the plurality of measured parameters ranges from 2 to about 300,000 measured parameters.

[0023] Systems for practicing the subject methods are also provided. The system, according to certain embodiments, comprises an apparatus configured to acquire cytometric data including measurements of a plurality of parameters from illuminated particles in a sample flowing in a flow stream, and a processor including a memory operatively coupled to the processor, the memory including instructions stored on the memory that, when executed by the processor, cause the processor to identify a parameter of interest, specify positive and negative measurement intervals for the parameter of interest, and scale the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0024] In embodiments of the subject system, the system is configured to display the scaled cytometric data on a display device. In such embodiments, the system is configured to display the scaled cytometric data on a display device by causing the display of a plot of the cytometric data including the transformed parameters of interest. In some embodiments, the system is configured to identify one or more additional parameters of interest, specify positive and negative measurement intervals for each additional parameter of interest, and scale the cytometric data by transforming each additional parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In some embodiments, the system is configured to cluster the cytometric data by applying a clustering algorithm to the scaled cytometric data. In other embodiments, the system is configured to cause the display of clusters of the scaled cytometric data. In still other embodiments, the cytometric data of the sample includes measurements obtained from a flow cytometer configured to analyze the sample.

[0025] A non-transitory computer-readable storage medium according to certain embodiments has instructions stored thereon, including an algorithm for acquiring cytometric data including measurements of a plurality of parameters from illuminated particles in a sample flowing in a flow stream, an algorithm for identifying a parameter of interest, an algorithm for designating positive and negative measurement intervals for the parameter of interest, and an algorithm for scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding designated positive and negative measurement intervals. [Brief explanation of the drawings]

[0026] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0027] [Figure 1-1] 10 shows an exemplary histogram display of measurements of a parameter of interest of cytometric data, according to an embodiment of the present invention. [Figure 1-2] 10 shows an exemplary histogram display of measurements of a parameter of interest of cytometric data, according to an embodiment of the present invention. [Figure 2] 2 illustrates an exemplary cumulative distribution function 200 in accordance with certain embodiments. [Figure 3] FIG. 1 illustrates a functional block diagram of an example control system for a particle analyzer, in accordance with certain embodiments. [Figure 4] 1 illustrates a flow cytometer according to certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of a particle analysis system for sample analysis and particle characterization, according to certain embodiments. [Figure 6A] 1 shows a schematic diagram of a particle analyzer and sorter system, according to certain embodiments. [Figure 6B] 1 shows a schematic diagram of a particle analyzer and sorter system, according to certain embodiments. [Figure 7] 1 illustrates a block diagram of a computing system in accordance with certain embodiments. [Figure 8] 1 shows a two-dimensional plot illustrating two parameters of cytometric data scaled according to a default scaling approach and scaled according to an embodiment of the present invention. [Figure 9] 1 shows a two-dimensional plot illustrating two parameters of cytometric data scaled according to a default scaling approach and scaled according to an embodiment of the present invention. [Figure 10]1 shows a two-dimensional plot illustrating two parameters of cytometric data scaled according to a default scaling approach and scaled according to an embodiment of the present invention, as well as the results of applying a clustering algorithm to the scaled cytometric data. DETAILED DESCRIPTION OF THE INVENTION

[0028] Aspects of the present disclosure include methods for scaling cytometric data. In embodiments, the method includes acquiring cytometric data of a sample including measurements of multiple parameters from illuminated particles in the sample flowing in a flow stream, identifying a parameter of interest, specifying positive and negative measurement intervals for the parameter of interest, and scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In other cases, the method includes rescaling the specified negative measurement interval of the parameter of interest. In yet other cases, the method includes rescaling the specified positive measurement interval of the parameter of interest. If desired, the method also includes clustering the cytometric data by applying a clustering algorithm to the scaled cytometric data. Systems for practicing the subject methods are also provided. Non-transitory computer-readable storage media are also described.

[0029] Before the present invention is described in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0030] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any other stated or intervening value in the stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may individually be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0031] 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 a number that is near or approximately the number preceded by the term. When determining whether a number is near or approximately a specifically recited number, the near or approximately unrecited number may be a number that, in the context in which it is presented, provides a substantial equivalent to the specifically recited number.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill 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 illustrative methods and materials are described below.

[0033] All publications and patents cited herein are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited, as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. 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 publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0034] It should be noted that, as used in this specification and the appended claims, the singular forms "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 element. Accordingly, 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.

[0035] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other several 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.

[0036] Although apparatus and methods have been or will be described with functional descriptions for the sake of grammatical fluidity, it is expressly understood that unless expressly recited under 35 U.S.C. § 112, the claims should not necessarily be construed as limited by construction of "means" or "step" limitations, but should be accorded the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of legal equivalents, and if the claims are expressly recited under 35 U.S.C. § 112, they should be accorded the full legal equivalents under 35 U.S.C. § 112.

[0037] As summarized above, the present disclosure provides a method for scaling cytometric data. In further describing embodiments of the present disclosure, a method is first described in more detail, including rescaling a specified negative measurement interval of a parameter of interest, rescaling a specified positive measurement interval of the parameter of interest, and clustering the cytometric data by applying a clustering algorithm to the scaled cytometric data. Next, a system for practicing the subject method is described. A non-transitory computer-readable storage medium is also described.

[0038] Method for scaling site metric data Aspects of the present disclosure include methods for scaling cytometric data. In particular, the present disclosure includes methods for acquiring cytometric data of a sample including measurements of multiple parameters from particles irradiated in the sample flowing in a flow stream, identifying a parameter of interest, specifying positive and negative measurement intervals for the parameter of interest, and scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. The term scaling is used to refer to the transformation of the relative distance between measurements of parameters in the cytometric data. In some cases, the ability to analyze high-dimensional cytometric data may be improved by displaying or otherwise analyzing scaled cytometric data in accordance with the present invention. Additionally, the effectiveness of discovering populations within the cytometric data, i.e., clustering the cytometric data, may be improved by scaling the cytometric data in accordance with the present invention, and analysis of such data may be improved by displaying or otherwise analyzing the results of clustering the scaled cytometric data. For example, when the cytometric data includes data regarding cells (i.e., when the particles in the sample are cells), applying the subject method may be useful for discovering specific populations of cells that may otherwise remain undetected and unanalyzed. When used in conjunction with analysis of samples by flow cytometry, the subject method can help mitigate the effects of measurement noise in particle analysis systems.

[0039] Site Metric Data In practicing the subject methods, cytometric data of a sample is obtained. The cytometric data includes measurements from illuminated particles in a sample flowing in a flow stream. For example, the cytometric data may include measurements of light detected when the sample is illuminated with a light source and light from the sample is detected with a light detection system having one or more photodetectors. In embodiments, such measurements of light may include measurements of light intensity. As described in detail below, in some embodiments, the cytometric data may include measurements of one or more of excitation light scattered by the particles along a generally forward direction, excitation light scattered by the particles along a generally sideways direction, and light emitted from fluorescent molecules or fluorescent dyes used to label the particles in one or more frequency ranges. In embodiments of the invention, obtaining cytometric data of a sample includes obtaining measurements from flow cytometric analysis of the sample.

[0040] In some embodiments, the sample is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, plant, fungus, or a subset of animal tissues, cells, or component parts, such as may be found in certain instances, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "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, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. A 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 a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases 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).

[0041] In some embodiments, the sample source is a "mammal" or "mammalian animal," terms used broadly to describe organisms within the mammalian family, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The methods may be applied to cytometric data of samples obtained from human subjects of both genders and at any stage of development (i.e., neonate, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. It should be understood that while the present invention may be applied to cytometric data of samples from human subjects, it may also be implemented to cytometric data of samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0042] In embodiments, a sample (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 broad range of wavelengths, e.g., spanning 50 nm or more, such as 100 nm or more, e.g., 150 nm or more, e.g., 200 nm or more, e.g., 250 nm or more, e.g., 300 nm or more, e.g., 350 nm or more, e.g., 400 nm or more, and 500 nm or more. 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 irradiating with a broadband light source, broadband light source protocols of interest may include, but are not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, an ultra-bright light emitting diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0043] In other embodiments, the method comprises irradiating with a narrowband light source emitting a specific wavelength or narrow range of wavelengths, such as a light source emitting light in a narrow range of wavelengths, e.g., 50 nm or less, e.g., 40 nm or less, e.g., 30 nm or less, e.g., 25 nm or less, e.g., 20 nm or less, e.g., 15 nm or less, e.g., 10 nm or less, e.g., 5 nm or less, e.g., 2 nm or less, including light sources emitting light of a specific wavelength (i.e., monochromatic light). When the method comprises irradiating with a narrowband light source, narrowband light source protocols of interest include, but are not limited to, narrow wavelength LEDs, laser diodes, or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0044] In certain embodiments, the method includes irradiating the sample with one or more lasers. As discussed above, the type and number of lasers will vary depending on the sample and the desired light collected, and can be gas lasers such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO 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 instances, the method includes irradiating the flow stream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In still other instances, the method includes irradiating the flowstream with 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 still other instances, the method includes irradiating the flowstream with 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 Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0045] The sample can be illuminated with one or more of the above light sources, including, for example, two or more light sources, for example, three or more light sources, for example, four or more light sources, for example, five or more light sources, and ten or more light sources. The light source can include a combination of any type of light source. For example, in some embodiments, the method includes illuminating the sample in the flowstream with a laser array, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0046] The sample may be illuminated with wavelengths ranging from 200 nm to 1500 nm, including, for example, 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample may be illuminated with wavelengths ranging from 200 nm to 900 nm. In other cases, if the light source includes multiple narrowband light sources, the sample may be illuminated with specific wavelengths ranging from 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is illuminated with specific wavelengths ranging from 200 nm to 700 nm, such as a laser array having a gas laser, excimer laser, dye laser, metal vapor laser, and solid-state laser, as described above.

[0047] When two or more light sources are used, the sample can be illuminated by the light sources simultaneously or sequentially, or a combination thereof. For example, the sample can be illuminated by each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When two or more light sources illuminate the sample sequentially, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, including, for example, 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, and 60 microseconds or more. For example, the method can include illuminating the sample with a light source (e.g., a laser) for a period ranging from 0.001 microseconds to 100 microseconds, including, for example, 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microseconds to 25 microseconds, and 5 microseconds to 10 microseconds. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source may be the same or different.

[0048] The period between illumination by each light source can also vary, as needed, separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., up to 10 microseconds or more, e.g., up to 15 microseconds or more, e.g., up to 30 microseconds or more, and up to 60 microseconds or more. For example, the period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., from 0.01 microseconds to 50 microseconds, e.g., from 0.1 microseconds to 35 microseconds, e.g., from 1 microsecond to 25 microseconds, and from 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0049] The sample can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating particles in the sample with a light source. In other cases, the sample therein is illuminated with a light source at discrete intervals, such as illuminating at discrete intervals including 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.

[0050] Depending on the light source, the sample may be illuminated from varying distances, including, for example, 0.01 mm or more, such as 0.05 mm or more, for example, 0.1 mm or more, such as 0.5 mm or more, for example, 1 mm or more, such as 2.5 mm or more, for example, 5 mm or more, for example, 10 mm or more, such as 15 mm or more, for example, 25 mm or more, and 50 mm or more. The angle or illumination may also vary, for example, from 15° to 85°, for example, from 20° to 80°, for example, from 25° to 75°, and from 10° to 90°, including, for example, from 30° to 60° at a 90° angle.

[0051] In certain embodiments, the method includes illuminating the sample with two or more beams of frequency-shifted light. A light beam generator component having a laser and an acousto-optical device for frequency-shifting the laser light may be employed. In these embodiments, the method includes illuminating the acousto-optical device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use in illuminating the sample in the flow stream), the lasers may have specific wavelengths varying from 200 nm to 1500 nm, including, for example, 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. The acousto-optical device may be illuminated with one or more lasers, including, for example, two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and ten or more lasers. The lasers may include any combination of multiple laser types. For example, in some embodiments, the method includes illuminating the acousto-optic device with a laser array, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0052] When two or more lasers are employed, the acousto-optical device can be irradiated with the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be irradiated with each of the lasers simultaneously. In other embodiments, the acousto-optical device is irradiated with each of the lasers sequentially. When two or more lasers are employed to sequentially irradiate the acousto-optical device, the time for which each laser irradiates the acousto-optical device can be individually 0.001 microseconds or more, including, for example, 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, and 60 microseconds or more. For example, the method can include irradiating the acousto-optical device with the laser for a duration ranging from 0.001 microseconds to 100 microseconds, including, for example, 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0053] The period between illumination by each laser can also vary, as desired, separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., up to 10 microseconds or more, e.g., up to 15 microseconds or more, e.g., up to 30 microseconds or more, and 60 microseconds or more. For example, the period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., from 0.01 microseconds to 50 microseconds, e.g., from 0.1 microseconds to 35 microseconds, e.g., from 1 microsecond to 25 microseconds, and e.g., from 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially by more than two (i.e., three or more) lasers, the delay between illumination by each laser can be the same or different.

[0054] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the acousto-optic device with a laser. In other cases, the acousto-optic device is illuminated with a laser at discrete intervals, including, for example, illumination every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0055] Depending on the laser, the acousto-optic device may be illuminated from different distances, including, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 2.5 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 15 mm or more, for example, 25 mm or more, and 50 mm or more. The angle or illumination may also vary, for example, from 15° to 85°, for example, from 20° to 80°, for example, from 25° to 75°, and from 10° to 90°, including, for example, from 30° to 60° at an angle of 90°.

[0056] In an embodiment, a method includes applying high frequency drive signals to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals can be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams, including, for example, three or more high frequency drive signals, for example, four or more high frequency drive signals, for example, five or more high frequency drive signals, for example, six or more high frequency drive signals, for example, seven or more high frequency drive signals, for example, eight or more high frequency drive signals, for example, nine or more high frequency drive signals, for example, ten or more high frequency drive signals, for example, fifteen or more high frequency drive signals, for example, twenty-five or more high frequency drive signals, for example, fifty or more high frequency drive signals, and one hundred or more high frequency drive signals.

[0057] The angularly deflected laser beams generated by the high frequency drive signals each have an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying high frequency drive signals having an amplitude sufficient to generate an angularly deflected laser beam at a desired intensity. In some cases, each applied high frequency drive signal independently has an amplitude of about 0.001 V to about 500 V, including, for example, about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to about 50 V, e.g., about 2 V to about 40 V, e.g., about 3 V to about 30 V, and about 5 V to about 25 V. In some embodiments, each applied high frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, including, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, and about 5 MHz to about 50 MHz.

[0058] In these embodiments, the angularly deflected laser beams within the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, e.g., by 0.005 μm or more, e.g., by 0.01 μm or more, e.g., by 0.05 μm or more, e.g., by 0.1 μm or more, e.g., by 0.5 μm or more, e.g., by 1 μm or more, e.g., by 5 μm or more, e.g., by 10 μm or more, e.g., by 100 μm or more, e.g., by 500 μm or more, e.g., by 1000 μm or more, and by 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams, for example, along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) can be, for example, an overlap of 0.005 μm or more, for example, an overlap of 0.01 μm or more, for example, an overlap of 0.05 μm or more, for example, an overlap of 0.1 μm or more, for example, an overlap of 0.5 μm or more, for example, an overlap of 1 μm or more, for example, an overlap of 5 μm or more, for example, an overlap of 10 μm or more, and an overlap of 100 μm or more.

[0059] In certain instances, the flow cytometry system of the present invention may be implemented using a method similar to that described in Diebold, et al., Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Patent 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, 10,620,111, and U.S. Patent Nos. A flow cytometry system configured to image particles in a flow stream by fluorescence imaging using radio frequency tagged emission (FIRE), such as those described in 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.

[0060] As discussed above, in embodiments, light from the illuminated sample is transmitted to a light detection system and measured by one or more photodetectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of the following wavelengths: 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, the method includes measuring wavelengths of light corresponding to the fluorescence peak wavelengths of the fluorophores, hi some embodiments, the method includes measuring the collected light across the fluorescence spectrum of each fluorophore in the sample.

[0061] The collected light can be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured 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.

[0062] Measurement of collected light can be made one or more times during the subject methods, including, for example, two or more times, for example, three or more times, for example, five or more times, and ten or more times. In certain embodiments, light propagation is measured two or more times, and in certain cases, the data is averaged.

[0063] The light from the sample may be measured at one or more of these wavelengths, for example, at 5 or more different wavelengths, for example, at 10 or more different wavelengths, for example, at 25 or more different wavelengths, for example, at 50 or more different wavelengths, for example, at 100 or more different wavelengths, for example, at 200 or more different wavelengths, for example, at 300 or more different wavelengths, and including measuring collected light at 400 or more different wavelengths.

[0064] Identify the parameter of interest and specify positive and negative measurement intervals Practicing the subject methods includes identifying a parameter of interest. A parameter refers to one of a plurality of characteristics measured within a sample and comprising cytometric data. Any measurable characteristic of interest may include a parameter. For example, the parameter may correspond to measurements of a particular wavelength range of light measured from illuminated sample particles. In some cases, the parameter may correspond to a detector channel used to detect a particular wavelength of light from illuminated sample particles. In other cases, the parameter may correspond to a particular fluorescent light emitted from the sample particles. In embodiments of the subject methods, the plurality of measured parameters ranges from 2 to approximately 300,000 measured parameters.

[0065] Identifying a parameter of interest refers to selecting one of a plurality of parameters for scaling according to the subject method. Embodiments of the subject method sequentially scale the cytometric data with respect to each parameter of a plurality of parameters comprising the cytometric data. Thus, identifying a parameter may include identifying one parameter from a list of parameters to be scaled. The parameter of interest may be identified based on any applicable criteria. In some cases, when the particles comprising the sample are cells, the parameter of interest may be identified based on one or more types of cells that may be present in the sample and / or one or more fluorescent dyes or labels applied to the sample.

[0066] The subject method further includes specifying positive and negative measurement intervals for the parameter of interest. A measurement interval refers to a range of potential measurement values, such as a continuous series of potential measurement values, for the parameter of interest. In embodiments, the measurement interval may be bounded or unbounded. In embodiments, a bounded interval is an interval that has a maximum and minimum measurement value, i.e., values ​​that bound the interval. In embodiments, an unbounded interval is an interval that has neither a maximum nor a minimum potential measurement value. A positive measurement interval refers to an interval of potential measurements of the parameter of interest where the potential measurement value indicates the presence of a particular characteristic. A negative measurement interval refers to an interval of potential measurements of the parameter of interest where the potential measurement value indicates the absence of such characteristic. In some cases, the positive and negative measurement intervals are contiguous. For example, in embodiments, the maximum value of a negative measurement interval may correspond to the minimum value of the positive range. The maximum and minimum values ​​of a measurement interval refer to the upper and lower limits of the measurement interval, respectively. In such a continuous configuration, only three points may be used to specify the positive and negative measurement intervals: the point at the minimum of the negative measurement interval, the point at the maximum of the negative measurement and the minimum of the positive measurement interval, and finally the point at the maximum of the positive measurement range. In some cases, the minimum or lower limit of the negative measurement interval is n - The maximum or upper limit of the negative measurement interval is called n + and the maximum or upper limit of the positive measurement interval is called p.

[0067] FIG. 1 illustrates an exemplary histogram 100 of measurements of a parameter of interest for cytometric data, according to an embodiment of the present invention. The figure shows potential measurements of the parameter of interest on an x-axis 110. The y-axis 120 of the histogram shows the event count at each corresponding measurement. Plot 130 shows a continuous line representing the number of events at different measurements of the parameter of interest on the x-axis 110. Also shown are an exemplary negative measurement interval 140 and an exemplary positive measurement interval 150. The negative measurement interval is defined by a lower limit 160 and an upper limit 170. The positive measurement interval is defined by a lower limit 170 and an upper limit 180. Here, the positive and negative measurement intervals are continuous ranges, and the upper limit of the negative measurement interval 170 is the same as the lower limit of the positive measurement interval 170.

[0068] In embodiments, any convenient means for selecting positive and negative measurement intervals, such as positive and negative measurement intervals 150 and 140, may be applied. In some cases, the positive and negative measurement intervals may be selected based on visual inspection. For example, the positive and negative measurement intervals may be selected based on visual inspection of a plot of measurements of the parameter of interest, such as plot 100. That is, in some cases, the plot of the parameter of interest may be inspected for characteristic features indicative of positive or negative measurement intervals, and one or both of the positive and negative measurement intervals may be selected based on such characteristics. In embodiments, specifying at least one of the positive and negative measurement intervals for the parameter of interest includes performing one-dimensional gating to specify the measurement interval. Performing gating refers to determining a range of potential measurement values ​​corresponding to the feature of interest in the underlying particle, where the measurement values ​​comprise cytometric data. That is, gating refers to defining specific boundaries within the parameter of interest such that measurements that fall within the boundaries correspond to the particle of interest. Any convenient method for determining a gate for the parameter of interest may be applied. Because such gates are applied only to measurements of the parameter of interest, such gates are referred to as one-dimensional gates.

[0069] In some cases, a positive measurement interval may be determined by calculating a probability corresponding to each measurement of a parameter of interest for an event comprising cytometric data. Such a calculated probability represents the likelihood that the measurement exhibits a particular characteristic, i.e., the characteristic of interest. In other words, each measurement of a parameter of interest is assigned a probability of whether the measurement exhibits the characteristic of interest. In some cases, the probabilities associated with each parameter of interest may be combined to determine a positive measurement interval. Any convenient means for calculating the probability that a measurement exhibits a particular characteristic may be employed, and may include, for example, calculating a probability based on or otherwise taking into account other characteristics of the event, such as data obtained from other events within the cytometric data, e.g., a histogram of measurements of the parameter of interest from other events within the cytometric data, cytometric data from other samples, e.g., measurements of parameters other than the parameter of interest. Similarly, in embodiments, a negative measurement interval may be determined based on a calculated probability that the measurement of the parameter of interest does not exhibit a particular characteristic. In some cases, a negative measurement interval may be identified as a different interval from a positive measurement interval. In embodiments, the probability that a measurement does not exhibit the characteristic of interest may be calculated based on whether the measurement falls within such a negative measurement interval. In some cases, the probability that a measurement does not exhibit the characteristic of interest may be calculated based on whether the measurement falls within an interval that is not a positive measurement interval.

[0070] In contrast to performing one-dimensional gating on a parameter of interest to determine a positive measurement interval and / or a negative measurement interval, other techniques for designating a positive measurement interval and / or a negative measurement interval may automatically determine such intervals. Such techniques include, but are not limited to, applying a fluorescence minus one control technique, applying a mathematical model, or applying a machine learning algorithm. Automatically determining a positive measurement interval and / or a negative measurement interval means that the technique itself (e.g., a mathematical model only, not, for example, a full or partial specification from a user) generates a prediction of a positive measurement interval and / or a negative measurement interval based at least in part on cytometric data including the parameter of interest. Techniques including applying a fluorescence minus one control, applying a mathematical model, and applying a machine learning algorithm, in each case for designating one or both of the positive measurement interval and / or the negative measurement interval, are described in further detail below.

[0071] In other embodiments, designating at least one of a positive and a negative measurement interval for a parameter of interest includes applying a fluorescence minus one control to designate the measurement interval. A fluorescence minus one control, also known as an FMO control, refers to a technique well known in the art that applies all but one of the fluorescent dyes, stains, etc., applicable to the particles of the sample. Based on the cytometric data obtained from the sample so prepared, this technique can be used to identify a threshold that distinguishes between background fluorescence and meaningful results. Such a threshold can then be applied to select and designate at least one of a positive and a negative measurement interval. In embodiments, applying the fluorescence minus one technique results in finding only cells that are negative for a given parameter, i.e., cells that do not exhibit a specific characteristic in the given parameter. As a result, the fluorescence minus one technique can be used to define the interpercentile range of the negative distribution, which can be taken as the negative measurement interval (i.e., the negative range).

[0072] In yet other embodiments, specifying at least one of a positive and negative measurement interval for the parameter of interest includes applying a mathematical model to specify the measurement interval. A mathematical model refers to any convenient model, such as a calculation-based model, that can identify estimated positive and / or negative measurement intervals. In some cases, the mathematical model may take into account only cytometric data corresponding to the parameter of interest. In other cases, the mathematical model may take into account not only the parameter of interest but also cytometric data corresponding to other parameters of the cytometric data. In still other cases, the mathematical model may consider cytometric data collected based on other samples of other experimental data. The mathematical model may be an iterative mathematical model designed to iteratively refine the definition of the positive and / or negative measurement intervals subject to certain constraints. In some embodiments, the mathematical model may take into account the measured noise characteristics of the detection channel, the spillover matrix, and / or the cellular expression profile.

[0073] In other embodiments, assigning one or both of the positive and negative measurement intervals for the parameter of interest includes applying a machine learning algorithm to assign the measurement intervals. A machine learning algorithm refers to a convenient computer algorithm designed to learn automatically through experience. In embodiments, the associated machine learning algorithm may predict the positive and / or negative measurement intervals using supervised learning, unsupervised learning, or reinforcement learning approaches. The associated machine learning algorithm may use regression and classification techniques to arrive at a prediction of the positive and / or negative measurement intervals. In embodiments, the associated experience used to train such a learning algorithm may include, for example, specifically constructed training data or previously collected cytometric data or parameters other than the parameter of interest, or a combination thereof.

[0074] Scaling Site Metric Data Performing the subject method further includes scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In some cases, transforming the parameter of interest means adjusting the scale of the measurement value applicable to the parameter of interest. Embodiments of the present invention transform the parameter of interest based at least in part on each of the positive and negative measurement intervals.

[0075] An objective of some embodiments of the subject methods is to reduce the deleterious effects of background noise, or measurement noise, which can make cytometric data, particularly high-dimensional cytometric data, more difficult to analyze. Reducing the deleterious effects of background noise can be achieved in embodiments of the subject methods, in part, by transforming the parameter of interest to reduce the standard deviation of negative measurement intervals. This has the effect of compressing the background noise in the data. Thus, in some embodiments, transforming the parameter of interest includes rescaling a specified negative measurement interval of the parameter of interest. In some such embodiments, rescaling a specified negative measurement interval of the parameter of interest includes reducing the standard deviation of a specified negative measurement interval of the parameter of interest.

[0076] Furthermore, with respect to positive measurement intervals, embodiments of the subject method can improve the ability to analyze cytometric data by rescaling the positive measurement intervals. Rescaling the positive measurement intervals can, in some cases, have the effect of improving the effectiveness of clustering algorithms applied to cytometric data that include a parameter of interest. Accordingly, in some embodiments, transforming the parameter of interest further includes rescaling the specified positive measurement interval of the parameter of interest. In such embodiments, rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size. Any predetermined size can be applied. In embodiments, the predetermined size is the size of the negative measurement interval. In other embodiments, the predetermined size is the size of the positive measurement interval for a different parameter, i.e., a parameter of the plurality of parameters that is not the parameter of interest. In some cases, the predetermined size is selected such that the scaled positive measurement intervals are the same size for each scaled parameter of the plurality of parameters that comprise the cytometric data.

[0077] In certain embodiments, scaling the cytometric data includes differentially transforming each measurement of the parameter of interest in the cytometric data based on the probability of the measurement exhibiting the characteristic of interest. That is, each measurement of the parameter of interest for an event in the cytometric data is differentially transformed based at least in part on the calculated probability (as described above) that the measurement exhibits the particular characteristic. In such cases, scaling the parameter of interest is achieved by transforming each measurement of the parameter of interest in the cytometric data, where such transformation is a differential transformation based at least in part on the probability that each measurement exhibits the particular characteristic.

[0078] In certain embodiments, transforming the parameter of interest includes adaptively scaling the parameter of interest accordingly based on a predetermined mathematical approach. For example, in some cases, transforming the parameter of interest includes adaptively scaling the parameter of interest according to the following equation:

[0079]

number

[0080] wherein each of the terms in the above formula has the following meaning:

[0081] s(x) represents an adaptively scaled measurement of the parameter of interest, i.e., an output value resulting from a transformation of the parameter of interest based at least in part on corresponding specified positive and negative measurement intervals.

[0082] x represents the unscaled measurement value of the parameter of interest, i.e., x represents the value of the parameter of interest contained in the acquired cytometric data, in other words, x represents the raw data value.

[0083] (n - ,n + ) is the specified negative measurement interval of the parameter of interest. In other words, the measurement value n of the parameter of interest - is the lower limit of the negative measure, and the measured value of the parameter of interest, n + is the upper limit of the negative measurement interval.

[0084] (n + , p) is the specified positive measurement interval of the parameter of interest. In other words, the measurement value n of the parameter of interest +is the lower limit of the positive measurement interval, and the measurement value p of the parameter of interest is the upper limit of the positive measurement interval. Because the upper limit of the negative measurement interval and the lower limit of the positive measurement interval are the same measurement value, the negative measurement interval and the positive measurement interval may be consecutive, with the negative measurement interval having a lower measurement value than the positive measurement interval. Also, once the negative measurement interval is defined, the positive measurement interval can be defined by specifying only a single measurement value that is the upper limit of the positive measurement interval.

[0085] c is the compression ratio. Qualitatively, the compression ratio is a number used to determine how much larger the signal should be compared to the noise in the context of upscaling positive values. That is, positive values ​​(such as values ​​with a Z-score of 3 or greater) are upscaled by multiplying them by the compression ratio, also called the noise compression ratio. Setting the compression ratio to the smallest possible compression ratio value of 1.0 means that no compression is applied to the transform. In some cases, the compression ratio is set to a value of 50. In certain embodiments, the default compression ratio is set to a value of 70.

[0086] The bar X is the median value of the negative measurement interval, i.e., the bar X is the middle value of the aforementioned negative measurement interval.

[0087] SD is the standard deviation of the negative measurement interval and is calculated as follows, where IQR is the interquartile range of the negative measurement interval. Interquartile range means the range between the 75th and 25th percentiles of the negative measurement interval (i.e., the range between the upper and lower quartiles of the negative measurement interval).

[0088]

number

[0089] z(x) is the z-transform according to

[0090]

number

[0091] In some cases, the effect of applying a z-transform to the raw data values ​​of a parameter of interest is to make the mean of the resulting transformed values ​​equal to zero and the standard deviation of the transformed values ​​equal to one.

[0092] g(z) is the inverse hyperbolic sine function according to

[0093]

number

[0094] In other words, the values ​​are further transformed using the arcsinh function. Such functions are known in the art and are, for example, a basic variation of the gLog function used for CyTOF data transformation.

[0095]

number

[0096] is μ=z(n + ) and σ=1. This CDF is used in connection with the transition between positive and negative measurement intervals. In particular, the CDF facilitates smoothing the transition between positive and negative measurement intervals by weighting the amount of scaling using a sigmoid function, e.g., a CDF centered around a noise cutoff, e.g., a transformation value of 3.0. In some cases, a smoothed transition may be justified due to the transition between an unscaled negative measurement interval and a scaled positive measurement interval.

[0097] Figure 2 shows an example cumulative distribution function 200 according to the cumulative distribution functions described above. Figure 2 shows potential z-transformed measurements of the parameter of interest on the x-axis 210. The y-axis 220 of the plot shows the cumulative distribution function at each corresponding z-transformed value. The upper limit of the negative measurement interval, n +, the Z-transform value of the measurement of the parameter of interest is taken along the x-axis, e.g., the mean value of the cumulative distribution function, z(n + ) can be seen. The cumulative distribution function is shown in symbolic form 230. As discussed above, the cumulative distribution function may be applied to smooth the transition between the transformed negative and positive measurement intervals. Such smoothing is enabled based in part on the sigmoidal shape of the plot of the cumulative distribution function 240.

[0098] Cytometric Data Display, Analysis, and Clustering Performing the subject methods may, in some embodiments, further include displaying the scaled cytometric data. Any convenient display format may be employed. For example, any display technique used to display unscaled cytometric data according to the subject methods may be employed to display cytometric data scaled according to the subject methods. In embodiments, the scaled cytometric data may be displayed, for example, on a one-dimensional plot, with the x-axis representing measurements scaled according to the subject methods and the y-axis indicating event counts corresponding to each scaled measurement. In other embodiments, the scaled cytometric data may be displayed, for example, on a two-dimensional plot, with each axis representing a measurement scaled according to the subject methods and the event counts represented, for example, by color or grayscale shades used to display points or regions on the two-dimensional plot. In embodiments, displaying the scaled cytometric data includes displaying a plot of the cytometric data including the transformed parameter of interest. That is, the scaled parameter of interest may be displayed as one dimension, i.e., the x-axis, in a one-dimensional plot, or as one or two dimensions, i.e., the x-axis or y-axis, in a two-dimensional plot.

[0099] Embodiments of the subject methods may further include identifying one or more additional parameters of interest, specifying positive and negative measurement intervals for each additional parameter of interest, and scaling the cytometric data by transforming each additional parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In other words, embodiments of the subject methods may include scaling two or more parameters of the cytometric data. Thus, the steps of identifying additional parameters of interest, specifying positive and negative measurement intervals, and scaling the data by applying a transformation may be performed for the additional parameters of interest in a manner similar to that used for a single parameter of interest, as described above. Any number of additional parameters of interest may be identified and scaled. In some cases, only a subset of the parameters comprising the cytometric data may be identified and scaled. In other cases, all parameters comprising the cytometric data may be identified and scaled. In some embodiments of the subject methods in which two or more parameters of the cytometric data are scaled, the positive measurement intervals are scaled to the same size. In some cases, scaling the positive measurement intervals to the same size may facilitate analysis of the cytometric data, for example, by improving the effectiveness of clustering algorithms or reducing the effects of noise. This is because scaling each positive measurement interval to the same size can have the effect of equalizing the contributions of both weak and strong markers in the cytometric data.

[0100] Certain embodiments of the subject methods further include analyzing the scaled data. Scaled data refers to data that has been transformed according to the subject methods. In some cases, scaled data may include data that has been differentially transformed based on a calculated probability that measurements of a parameter of interest for events comprising the cytometric data exhibit a particular characteristic. Analyzing the transformed data refers to studying, understanding, and / or characterizing the cytometric data. For example, analyzing the transformed data may include analyzing such data using at least a first data analysis algorithm. In other cases, analyzing the transformed data may include analyzing such data using a first data analysis algorithm, as well as one or more additional data analysis algorithms. The data analysis algorithm may be any convenient or useful algorithm for use in drawing inferences from the cytometric data. For example, in some embodiments, the data analysis algorithm may involve a data clustering algorithm, as described below. In other embodiments, the data analysis algorithm may include a dimensionality reduction algorithm, a feature extraction algorithm, a pattern recognition algorithm, etc., for example, to facilitate visualization of multidimensional data.

[0101] Other embodiments of the subject method further include clustering the cytometric data by applying a clustering algorithm to the scaled cytometric data. That is, the scaled cytometric data is used as input to the clustering algorithm, which identifies clusters within the scaled cytometric data, as opposed to, for example, the raw cytometric data. Clustering refers to any algorithm, technique, or method used to identify subpopulations of data within the cytometric data, where each element of the subpopulation shares certain characteristics with each other element of the subpopulation.

[0102] Any convenient clustering algorithm can be applied to identify clusters in the scaled cytometric data. In some cases, population clusters can be identified (along with gates that define the boundaries of the population) and can be determined automatically. Examples of methods for automatic gating are described in, for example, U.S. Patent Nos. 4,845,653, 5,627,040, 5,739,000, 5,795,727, 5,962,238, 6,014,904, and 6,944,338, and U.S. Patent Publication No. 2012 / 0245889, each of which is incorporated herein by reference.

[0103] In some cases, assigning particles of the scaled cytometric data to clusters involves applying a technique known in the art called k-means clustering. "k-means clustering" refers to a known partitioning technique that aims to divide each event or cell data point of a test sample into k clusters such that each data point belongs to the cluster with the closest mean. The technique of k-means clustering, including various general embodiments utilizing k-means clustering, is further described in L. M. Weber and M. D. Robinson, "Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data," Cytometry, Part A, Journal of Quantitative Cell Science, Vol. 89, Issue 12, pp. 1084-96, the entire contents of which are incorporated herein by reference.

[0104] In other cases, assigning particles of the scaled cytometric data to clusters involves applying a technique known in the art called the application of self-organizing maps. "Self-organizing maps" refers to the application of a type of artificial neural network algorithm that generates a map as a result of a neural network training step, where the map includes a collection of clusters that define the data points or cells of the sample. Techniques for applying self-organizing maps, including general embodiments of FlowSOM, are further described in L. M. Weber and M. D. Robinson, "Comparison of Clustering Methods for High-Dimensional Single-Cell Flow and Mass Cytometry Data," Cytometry, Part A, Journal of Quantitative Cell Science, Vol. 89, Issue 12, pp. 1084-96, which are incorporated herein by reference in their entireties. Other known or yet-to-be-discovered clustering techniques or algorithms may be applied as needed.

[0105] In still other cases, assigning particles of the scaled cytometric data to clusters involves applying a technique known in the art as the X-shift population discovery algorithm. The X-shift algorithm, as well as its practical applications, is further described in N. Samusik, Z. Good, M.H. Spitzer, K.L. Vis & G.P. Nolan (2016), Automated mapping of phenotype space with single-cell data. Nature methods, Vol. 13, Issue 6, p. 493, which is incorporated herein by reference in its entirety. Other known or yet-to-be-discovered clustering techniques or algorithms can be applied as needed.

[0106] In some cases, scaled cytometric data is used to improve the performance of clustering algorithms applied to the cytometric data. That is, clustering algorithms may perform better when applied to cytometric data scaled according to the subject methods, as opposed to raw, unscaled cytometric data. Better performance means that a clustering algorithm applied to scaled cytometric data can identify subpopulations of particles, such as cells, that would otherwise go undetected. That is, a clustering algorithm applied to scaled cytometric data can identify distinct subpopulations of data that would otherwise be clustered into a single, larger subpopulation. Generally, a clustering algorithm performs better when the results produced by the clustering algorithm more closely reflect the physical characteristics of the sample from which the cytometric data was generated.

[0107] In some cases, scaled cytometric data are used to reduce the effects of measurement noise. Measurement noise refers to signals generated during collection of cytometric data that do not correspond to physical properties of the underlying sample, but instead correspond to instrumentation issues or random or unknown causes. In some cases, the scaled cytometric data reduces the amount of noise in the data, thereby improving the performance of clustering algorithms when applied to the scaled cytometric data. For example, in some embodiments of the subject methods, the particles of the sample are cells. In such embodiments, the scaled cytometric data may be used to distinguish between two similar cell populations.

[0108] In embodiments, displaying the scaled cytometric data includes displaying clusters of the scaled cytometric data. That is, when scaled cytometric data is displayed according to the present methods, information identifying the clusters on the display may also be displayed. The clusters may be identified on the display using any convenient technique, such as labeling, color coding, or other methods. In some cases, if the use of scaled cytometric data improves the performance of a clustering algorithm applied to the cytometric data, displaying clusters in the scaled cytometric data may facilitate distinguishing between two populations of particles that might otherwise have gone undetected. That is, for example, two types of cells may have been grouped into a single cell cluster by a clustering algorithm applied to unscaled cytometric data, but the clustering algorithm, when applied to the cytometric data, may instead identify the two types of cells by clustering them into their own clusters. Each cluster is then displayed on a display of the clustered data for visual review and further analysis.

[0109] A system for scaling site metric data As summarized above, aspects of the present disclosure include a system configured to scale cytometric data. The system, according to certain embodiments, comprises an apparatus configured to acquire cytometric data including measurements of multiple parameters from illuminated particles in a sample flowing in a flow stream, and a processor including a memory operatively coupled thereto, the memory having instructions stored thereon that, when executed by the processor, cause the processor to identify a parameter of interest, specify positive and negative measurement intervals for the parameter of interest, and scale the cytometric data by converting the parameter of interest based, at least in part, on the corresponding specified positive and negative measurement intervals. Scaling the cytometric data by converting the parameter of interest based, in part, on the positive and negative measurement intervals is described above. Systems and devices for use in collecting measurements including cytometric data are described below.

[0110] light source In embodiments, for cytometric data, particles in a sample flowing within a flow stream can be illuminated using a light source. The light source can be any suitable broadband or narrowband light source. Depending on the components within the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit wavelengths of light that vary over a range of 200 nm to 1500 nm, including, for example, 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. For example, the light source can include a broadband light source that emits light having a wavelength of 200 nm to 900 nm. In other cases, the light source includes a narrowband light source that emits wavelengths over a range of 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) that emits light having a wavelength over a range of 200 nm to 900 nm. In certain embodiments, the light source is a laser. In some cases, the subject systems include a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO 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 cases, the subject systems include a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, 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 still other instances, the subject systems include solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, slim YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0111] In other cases, the light source is a non-laser light source that is a lamp, including but not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode, such as a broadband LED with a continuous spectrum, a high brightness light emitting diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated light source, etc. In some cases, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0112] The light source can be positioned at any suitable distance from the sample (e.g., a flow stream in a flow cytometer), such as at a distance of 0.001 mm or more from the flow stream, including at a distance of 0.005 mm or more, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 5 mm or more, such as 10 mm or more, such as 25 mm or more, and 100 mm or more. Furthermore, the light source can illuminate the sample at any suitable angle (e.g., relative to the perpendicular axis of the flow stream), such as an angle ranging from 10° to 90°, e.g., a 90° angle, including 15° to 85°, e.g., 20° to 80°, e.g., 25° to 75°, and 30° to 60°.

[0113] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to continuously illuminate the sample, such as with a continuous wave laser that continuously illuminates the flow stream at the interrogation point of the flow cytometer. In other cases, the subject systems may illuminate the sample using a light source configured to illuminate the sample at discrete intervals, including every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 milliseconds, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or at some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to provide intermittent illumination of the sample with the light source. For example, the subject systems in these embodiments may include one or more laser beam choppers, which are manual or computer-controlled beam stops, for blocking and exposing the sample to the light source.

[0114] In some cases, the light source is a laser. The laser of interest can include pulsed lasers or continuous wave lasers. For example, the laser can be 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; a dye laser such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, metal vapor lasers such as neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers, and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or frequency-doubled or frequency-tripled embodiments of any of the above lasers.

[0115] In certain cases, the light source is an optical beam generator configured to generate two or more beams of frequency-shifted light. In some cases, the optical beam generator includes a laser, a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optic device to generate two or more angularly polarized laser beams. In these embodiments, the laser may be a pulsed laser or a continuous wave laser, as described above.

[0116] The acousto-optic device can be any convenient acousto-optic device configured to frequency-shift laser light using applied acoustic waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject systems is configured to generate an angularly deflected laser beam from light from a laser and an applied high-frequency drive signal. This high-frequency drive signal can be applied to the acousto-optic device by any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0117] In some cases, the controller is configured to apply high frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams within the output laser beam, including the controller being configured to apply, for example, three or more high frequency drive signals, for example, four or more high frequency drive signals, for example, five or more high frequency drive signals, for example, six or more high frequency drive signals, for example, seven or more high frequency drive signals, for example, eight or more high frequency drive signals, for example, nine or more high frequency drive signals, for example, ten or more high frequency drive signals, for example, fifteen or more high frequency drive signals, for example, twenty-five or more high frequency drive signals, for example, fifty or more high frequency drive signals, and including being configured to apply one hundred or more high frequency drive signals.

[0118] In some cases, the controller is configured to apply a high frequency drive signal having an amplitude that varies from, for example, about 0.001 V to about 500 V, for example, about 0.005 V to about 400 V, for example, about 0.01 V to about 300 V, for example, about 0.05 V to about 200 V, for example, about 0.1 V to about 100 V, for example, about 0.5 V to about 75 V, for example, about 1 V to 50 V, for example, about 2 V to 40 V, for example, 3 V to about 30 V, and about 5 V to about 25 V to generate an angularly deflected laser beam intensity profile within the output laser beam. In some embodiments, each applied high frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, for example, from about 0.005 MHz to about 400 MHz, for example, from about 0.01 MHz to about 300 MHz, for example, from about 0.05 MHz to about 200 MHz, for example, from about 0.1 MHz to about 100 MHz, for example, from about 0.5 MHz to about 90 MHz, for example, from about 1 MHz to about 75 MHz, for example, from about 2 MHz to about 70 MHz, for example, from about 3 MHz to about 65 MHz, for example, from about 4 MHz to about 60 MHz, and from about 5 MHz to about 50 MHz.

[0119] In certain embodiments, the controller comprises a processor including a memory operatively coupled thereto, the memory having instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, or may include instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more angularly deflected laser beams having different intensities, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, or may include instructions for generating one hundred or more angularly deflected laser beams having different intensities.

[0120] In certain cases, the controller comprises a processor including a memory operatively coupled thereto, the memory having instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge of the output laser beam to its center along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam may range from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis, such as from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, including from about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other cases, the controller comprises a processor including a memory operatively coupled thereto, the memory having instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge of the output laser beam to its center along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam can range from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis, such as from 0.5% to about 95%, such as from 1% to about 90%, such as from about 2% to about 85%, such as from about 3% to about 80%, such as from about 4% to about 75%, such as from about 5% to about 70%, such as from about 6% to about 65%, such as from about 7% to about 60%, such as from about 8% to about 55%, and including from about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet other cases, the controller comprises a processor including a memory operatively coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In still other cases, the controller comprises a processor including a memory operatively coupled to the processor, the memory having instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having a top-hat shaped intensity profile along a horizontal axis.

[0121] In some cases, the subject optical beam generator can be configured to generate angularly polarized laser beams within the output laser beam that are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly polarized laser beams can be separated by 0.001 μm or more, e.g., by 0.005 μm or more, e.g., by 0.01 μm or more, e.g., by 0.05 μm or more, e.g., by 0.1 μm or more, e.g., by 0.5 μm or more, e.g., by 1 μm or more, e.g., by 5 μm or more, e.g., by 10 μm or more, e.g., by 100 μm or more, e.g., by 500 μm or more, e.g., by 1000 μm or more, and by 5000 μm or more. In some cases, the system is configured to generate angularly polarized laser beams within the output laser beam that partially overlap with adjacent angularly polarized laser beams, e.g., along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) can be, for example, an overlap of 0.005 μm or more, for example, an overlap of 0.01 μm or more, for example, an overlap of 0.05 μm or more, for example, an overlap of 0.1 μm or more, for example, an overlap of 0.5 μm or more, for example, an overlap of 1 μm or more, for example, an overlap of 5 μm or more, for example, an overlap of 10 μm or more, and an overlap of 100 μm or more.

[0122] In certain instances, optical beam generators configured to generate two or more beams of frequency-shifted light are disclosed in U.S. Patent 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, Nos. 10,451,538, 10,620,111, 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.

[0123] detector In embodiments, the cytometric data may consist of measurements of light detected from illuminated particles in a sample flowing in a flow stream (in some cases, the measurements include measurements of multiple parameters of the light). A light detection system may be employed to measure such light from particles in the sample. The light detection system may have one or more light detectors. Light detectors of interest may include, but are not limited to, optical sensors 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, phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, among other photodetectors. In certain embodiments, light from the sample is measured with 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.

[0124] In certain cases, a subject light detection system includes a plurality of light detectors. In some cases, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In particular cases, the light detection system includes a light detector array, such as a photodiode array. In these embodiments, the light detector array can include four or more light detectors, e.g., ten or more light detectors, e.g., twenty-five or more light detectors, e.g., fifty or more light detectors, e.g., one hundred or more light detectors, e.g., two hundred or more light detectors, e.g., five hundred or more light detectors, e.g., seven hundred or more light detectors, and one thousand or more light detectors. For example, the detector can be a photodiode array having four or more photodiodes, e.g., ten or more photodiodes, e.g., twenty-five or more photodiodes, e.g., fifty or more photodiodes, e.g., one hundred or more photodiodes, e.g., two hundred or more photodiodes, e.g., five hundred or more photodiodes, e.g., seven hundred or more photodiodes, and one thousand or more photodiodes.

[0125] The photodetectors can be arranged in any geometric configuration as desired, including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, oval, and irregularly patterned configurations. The photodetectors within the photodetector array can be oriented at angles ranging from 10° to 180° relative to another plane (as referenced to the XZ plane), including, for example, 15° to 170°, for example, 20° to 160°, for example, 25° to 150°, for example, 30° to 120°, and 45° to 90°. The photodetector array can be any suitable shape, including rectilinear shapes such as square, rectangular, trapezoidal, triangular, hexagonal, curvilinear shapes such as circular and oval, and irregular shapes such as a parabolic base joined to a planar top. In a particular case, the photodetector array has an active surface that is rectangular in shape.

[0126] Each photodetector (e.g., photodiode) in the array may have an active surface with a width ranging from 5 μm to 250 μm, for example, from 10 μm to 225 μm, for example, from 15 μm to 200 μm, for example, from 20 μm to 175 μm, for example, from 25 μm to 150 μm, for example, from 30 μm to 125 μm, and from 50 μm to 100 μm, and a length ranging from 5 μm to 250 μm, for example, from 10 μm to 225 μm, for example, from 15 μm to 200 μm, for example, from 20 μm to 175 μm, for example, from 25 μm to 150 μm, for example, from 30 μm to 125 μm, and from 50 μm to 100 μm, 2 ~10,000 μm 2 For example, 50 μm 2 ~9000μm 2 , e.g., 75 μm 2 ~8000μm 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 , and 200 μm 2 ~5000μm 2 is.

[0127] The size of the photodetector array can vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and its length can range from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, and 5 mm to 25 mm. The width of the photodetector array can also range from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, and 5 mm to 25 mm. Thus, the effective surface of the photodetector array can be 0.1 mm 2 ~10,000mm 2 For example, 0.5 mm2 ~5000mm 2 , e.g., 1 mm 2 ~1000mm 2 , e.g., 5 mm 2 ~500mm 2 , and 10mm 2 ~100mm 2 It could be.

[0128] The subject photodetectors are configured to measure collected light at one or more wavelengths, e.g., at two or more wavelengths, e.g., at five or more different wavelengths, e.g., at ten or more different wavelengths, e.g., at twenty-five or more different wavelengths, e.g., at fifty or more different wavelengths, e.g., at one hundred or more different wavelengths, e.g., at two hundred or more different wavelengths, e.g., at three hundred or more different wavelengths, including measuring light emitted by a sample in the flow stream at four hundred or more different wavelengths.

[0129] In some embodiments, the photodetector is configured to measure collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a range of wavelengths. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from a sample in the flowstream at one or more specific wavelengths. For example, the system may include one or more detectors configured to measure light at one or more of the following wavelengths: 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, the photodetector may be configured to pair with a particular fluorophore, such as one used with the sample in a fluorescence analysis, hi some embodiments, the photodetector is configured to measure the collected light across the fluorescence spectrum of each fluorophore in the sample.

[0130] The light detection system may be configured to measure light continuously or at discrete intervals. In some cases, the target light detector is configured to continuously measure collected light. In other cases, the light detection system may be configured to measure within 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, every 1000 milliseconds, or some other interval.

[0131] Processor and memory configuration A system according to the present disclosure comprises a processor including a memory operatively coupled thereto, the memory having instructions stored therein that, when executed by the processor, cause the processor to identify a parameter of interest, specify positive and negative measurement intervals for the parameter of interest, and scale the cytometric data by converting the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0132] In some cases, the processor and / or memory may be operatively connected to an apparatus configured to acquire cytometric data including measurements of multiple parameters from illuminated particles in a sample flowing within the flowstream. Such operative connection may take any convenient form such that the cytometric data may be acquired by the processor through any convenient input technique, for example, via a wired or wireless network connection, shared memory, bus, or similar communication protocol with the source of the cytometric data, such as an Ethernet connection or a Universal Serial Bus (USB) connection, a portable memory device, or the like.

[0133] In embodiments, after cytometric data is acquired (e.g., by or from a flow cytometer), the processor and memory are configured to identify a parameter of interest. The parameter of interest may be identified in any convenient manner. For example, in some cases, the processor and memory may be configured to receive input data corresponding to a selection that identifies a parameter of interest. In other examples, the processor and memory may be configured to select a parameter of interest from a list of potential parameters of interest, for example, by iterating through a list of one or more potential parameters. In some cases, the processor and memory may be configured to receive input regarding characteristics of a plot to be displayed, and may be further configured to identify the parameter of interest based on the characteristics of the plot.

[0134] In embodiments of systems according to the present disclosure, the processor and memory are further configured to designate positive and negative measurement intervals for the parameter of interest. Such positive and negative measurement intervals may be identified in any convenient manner. For example, in some cases, the processor and memory may be configured to receive input data corresponding to at least one range of measurement data corresponding to either a positive or negative measurement interval, or both measurement intervals. In other embodiments, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to use one-dimensional gating to designate at least one of the positive and negative measurement intervals for the parameter of interest. As described above, the characteristics of such a gate may be defined manually or automatically (e.g., algorithmically). For example, in such embodiments, the processor may be configured to receive as input a one-dimensional gate that designates positive and negative measurement intervals for the parameter of interest. In yet other embodiments, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to apply a fluorescence minus one control to designate at least one of the positive and negative measurement intervals for the parameter of interest. In some cases, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to apply a mathematical model to assign at least one of positive and negative measurement intervals for the parameter of interest. In other cases, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to apply a machine learning algorithm to assign one or both of positive and negative measurement intervals for the parameter of interest.

[0135] In embodiments of systems according to the present disclosure, the processor and memory are further configured to scale the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In some embodiments, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to transform the parameter of interest by rescaling the specified negative measurement intervals of the parameter of interest. In such embodiments, the memory has further instructions stored thereon that, when executed by the processor, cause the processor to rescale the specified negative measurement intervals of the parameter of interest by reducing the standard deviation of the specified negative measurement intervals of the parameter of interest.

[0136] In certain embodiments, the memory has additional instructions stored thereon that, when executed by the processor, cause the processor to further transform the parameter of interest by rescaling a specified positive measurement interval of the parameter of interest. In other embodiments, rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size. The predetermined size of the scaled positive measurement interval may be any convenient size and can be varied as desired. In some cases, the predetermined size is the size of the negative measurement interval. In other cases, the predetermined size is the size of the scaled positive measurement interval corresponding to a second parameter of the plurality of parameters.

[0137] In an embodiment, the memory includes further instructions stored in the memory that, when executed by the processor, cause the processor to transform the parameter of interest by adaptively scaling the parameter of interest according to:

[0138]

number

[0139] As explained in detail above with respect to the method according to the present disclosure, s(x) represents the adaptively scaled measure of the parameter of interest, x represents the unscaled measure of the parameter of interest, and (n - ,n + ) is the specified negative measurement interval of the parameter, and (n + , p) is the specified positive measurement interval of the parameter, c is the compressibility ratio, X is the median of the negative measurement interval, and SD is the standard deviation of the negative measurement interval, calculated according to the following formula, where IQR is the interquartile range of the negative measurement interval:

[0140]

number

[0141] z(x) is the z-transform according to

[0142]

number

[0143] g(z) is the inverse hyperbolic sine function according to

[0144]

number

[0145]

number

[0146] is μ=z(n + ) and σ=1. The noise compression ratio can be set to any convenient value and can be varied as needed. The minimum value for noise compression ratio is 1.0, and the default value may be set to 70.

[0147] In embodiments, the memory has further instructions stored therein that, when executed by the processor, cause the processor to display the scaled cytometric data on a display device. In such embodiments, the system is configured to display the scaled cytometric data on the display device by causing the display of a plot of the cytometric data including the transformed parameter of interest. Any convenient display device can be used, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma (PDP) display, a quantum dot (QLED) display, or a cathode ray tube display device. The processor and / or memory may be operably connected to the display device via a wired connection, such as a universal serial bus (USB) connection, or a wireless connection, such as a Bluetooth connection. In some cases, a two-dimensional plot showing measurements of at least two parameters comprising the cytometric data is displayed, at least one of the parameters being transformed, e.g., scaled, as described herein.

[0148] In embodiments, the memory has further instructions stored therein that, when executed by the processor, cause the processor to identify one or more additional parameters of interest, specify positive and negative measurement intervals for each additional parameter of interest, and scale the cytometric data by transforming each additional parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals. In other words, in some cases, the system may be configured to transform two or more parameters of the cytometric data by scaling the parameters as described herein. In some cases, the processor causes the scaled cytometric data to be displayed, for example, by displaying each of the scaled parameters as described herein on a two-dimensional or three-dimensional plot.

[0149] In other embodiments, the memory has further instructions stored therein that, when executed by the processor, cause the processor to cluster the scaled cytometric data by applying a clustering algorithm to the scaled cytometric data. The processor and memory may be configured to cluster the scaled cytometric data according to any convenient clustering algorithm, such as those described herein, including k-means clustering, self-organizing maps, X-Shift, or other known or yet to be discovered clustering routines. In such embodiments, the memory may have further instructions stored therein that, when executed by the processor, cause the processor to display clusters of the scaled cytometric data. In some cases, the system is configured to scale the cytometric data such that performance of the clustering algorithm applied to the cytometric data is improved. Performance of the clustering algorithm may be improved in any number of ways, for example, by allowing the clustering algorithm to discover additional populations of distinct particle types, such as distinct cells, or by identifying particles as belonging to clusters when such particles would otherwise be ignored by the clustering algorithm, i.e., do not belong to a particle population or meaningful cluster. In some cases, scaling the cytometric data as described herein can reduce the effect of noise on the measurements of the particles that make up the cytometric data, thereby improving the performance of the clustering algorithm. In certain embodiments, the system is configured to scale the cytometric data so that the effect of measurement noise is reduced. In other embodiments, the particles are cells. In such cases, the system is configured to distinguish between two similar cell populations based on the scaled cytometric data.That is, the system may be configured to distinguish between cell types that may otherwise be understood to belong to the same cell type, when in fact the cells are cells of different types.

[0150] In embodiments, the cytometric data is high-dimensional data. High-dimensional data means that the cytometric data includes a large number of different measurement parameters. In other words, the cytometric data represents measurements of many different properties of the particle. In some cases, the plurality of measurement parameters ranges from 20,000 to about 300,000 measurement parameters. In some embodiments, the cytometric data of the sample includes measurements obtained from a flow cytometer configured to analyze the sample. In some cases, the apparatus configured to obtain the cytometric data may be a flow cytometer, or in other cases, may be an input device, such as a wired or wireless input device, configured to obtain the cytometric data from the flow cytometer.

[0151] particle analyzer Inventive systems of the present disclosure further include an apparatus configured to acquire cytometric data comprising measurements of multiple parameters from illuminated particles in a sample flowing in the flowstream. In some embodiments of the system, the apparatus is configured to acquire the cytometric data by analyzing the sample by illuminating particles in the sample flowing in the flowstream. In other embodiments, the apparatus is an input device configured to acquire cytometric data from a particle analyzer or sorting system, such as those described herein.

[0152] 3 shows a functional block diagram of an example particle analyzer or sorting control system, such as an analysis controller, i.e., processor 300 operatively connected to memory, for analyzing and displaying data. Processor 300 can be configured to implement various processes for controlling the graphical display of data, including biological events.

[0153] The device 302 may be configured to acquire cytometric data, such as biological event data. For example, a flow cytometer may generate flow cytometric event data. In embodiments, the device may be, or may be operatively connected to, a particle analyzer or sorting system, such as a flow cytometer. The device 302 may be configured to provide the biological event data to the processor 300. A data communication channel may be included between the device 302 and the processor 300. The biological event data may be provided to the processor 300 via the data communication channel.

[0154] The processor 300 may be configured to receive bio-event data from the device 302. The bio-event data received from the device 302 may include flow cytometric event data. The processor 300 may be configured to provide a graphical display on the display device 306, including a display of one or more histograms of the cytometric data, a first plot of the bio-event data, or a plot showing cluster data of the cytometric data. For example, the processor 300 may be configured to cause the display device 306 to display scaled cytometric data. The processor 300 may further be configured to render a region of interest as a gate around a population of bio-event data shown by the display device 306, e.g., overlaid on the first plot. In some embodiments, the gate may be a logical combination of one or more image regions of interest depicted on a single-parameter histogram or bivariate plot. In some embodiments, the display may be used to display particle parameters. In some embodiments, the display may be used to display settings applicable to the device 302 in conjunction with the histogram of the cytometric data for use in calibrating the detector of the device 302.

[0155] The processor 300 may further be configured to display data on the display device 306 within the gate differently from other events in the data outside the gate. For example, the processor 300 may be configured to render the color of the biological event data contained within the gate distinct from the color of the biological event data outside the gate. The display device 306 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0156] The processor 300 may be configured to receive, from a first input device, adjustments to configuration settings of the apparatus 302. Such adjustments to configuration settings, when received by the processor 300 from the first input device, may be used to update settings of the apparatus 302, such as particle analyzer settings. For example, in an embodiment, gain settings of a channel detector may be adjusted to avoid saturation of the channel detector.

[0157] Additionally, processor 300 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, first input device may be implemented as a mouse 310. This mouse 310 may initiate a gate selection signal (e.g., by clicking on the desired gate while positioning a cursor thereon) to processor 300 that identifies a gate displayed or manipulated via display device 306. In some implementations, the first device may be implemented as a keyboard 308 or other means for providing input signals to processor 300, such as a touchscreen, a pen, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in FIG. 3, mouse 310 may include a right mouse button and a left mouse button, each of which may generate an activation event.

[0158] This triggering event may cause the processor 300 to change how the data is displayed, what portions of the data are actually displayed on the display device 306, and / or provide input to further processing, such as selecting a population for particle sorting.

[0159] In some embodiments, the processor 300 may be configured to detect when a gate selection is initiated by the mouse 310. The processor 300 may further be configured to automatically modify the visualization of the plot to facilitate the gating process. This modification may be based on a particular distribution of the biological event data received by the processor 300.

[0160] The processor 300 may be connected to a storage device 304. The storage device 304 may be configured to receive and store biological event data from the processor 300. The storage device 304 may be further configured to enable retrieval of biological event data, such as flow cytometric event data, by the processor 300.

[0161] The display device 306 may be configured to receive display data from the processor 300. The display data may include a plot of the biological event data and a gate delineating a section of the plot. The display device 306 may be further configured to modify the presented information according to input received from the processor 300 in conjunction with input from the apparatus 302, the storage device 304, the keyboard 308, and / or the mouse 310.

[0162] In some embodiments, the processor 300 can generate a user interface to receive example events for filtering. For example, the user interface can include controls for receiving example events or example images. The example events or images, or example 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.

[0163] 4 shows a system 400 for flow cytometry, according to an exemplary embodiment of the invention. The system 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. The flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence focusing lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0164] Pump lasers 415a-c emit light in the form of laser beams. The wavelengths of the laser beams emitted from pump lasers 415a-c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits light at 488 nm and reflects light at 633 nm. Beam splitter 445b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects light at 488 nm and 633 nm.

[0165] The laser beam is then directed to a focusing lens 420, which focuses the beam onto the portion of the fluid stream where the sample particles are located in a flow chamber 425. The flow chamber is the part of the fluidics system that directs particles in the stream, usually one at a time, into the focused laser beam for investigation. The flow chamber can comprise a flow cell in a benchtop flow cytometer or a nozzle tip in a stream-in-air cytometer.

[0166] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, with re-emission at a variety of different wavelengths depending on particle properties such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or in the particle. Fluorescence emission and diffracted, refracted, reflected, and scattered light can be routed through one or more of beam splitters 445a-g, bandpass filters 450a-e, longpass filters 455a-b, and fluorescence focusing lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0167] The fluorescence focusing lens 440 collects light emitted from particle-laser beam interactions and routes it toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow wavelength range to pass through the filter. For example, bandpass filter 450a 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, or from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light below a specified wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths of light above a specified wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits wavelengths of light above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorochrome. The filters may be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0168] 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 445g is a 620 short-pass beam splitter, meaning that beam splitter 445g transmits light wavelengths shorter than 620 nm and reflects light wavelengths longer than 620 nm in a different direction. In one embodiment, beam splitters 445a-445g can comprise optical mirrors, such as dichroic mirrors.

[0169] The forward scatter detector 430 is positioned slightly off-axis from the direct beam passing through the flow cell and is configured to detect diffracted light, or excitation light traveling mostly forward through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall particle size. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect diffracted and reflected light from the particle's surface and internal structure, and tends to increase as particle structure becomes more complex. Fluorescent emissions from fluorescent molecules associated with the particle may be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, side scatter detector 435, and fluorescence detector may be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0170] Those skilled in the art will recognize that a flow cytometer according to an embodiment of the present invention is not limited to the flow cytometer illustrated in Figure 4, 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.

[0171] During operation, the operation of the flow cytometer is controlled by controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by controller / processor 490. Although not explicitly shown, controller / processor 490 may include at least one general-purpose processor as well as multiple parallel processing units, and may be coupled to the detectors to receive output signals therefrom and to electrical and electromechanical components of flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided within the system. Memory 495, controller / processor 490, and I / O 497 may collectively be provided as an integral part of flow cytometer 410. In such an embodiment, a display may also form part of I / O functionality 497 for presenting experimental data, including one or more histograms of cytometric data, to a user of cytometer 400. Alternatively, memory 495 and some or all of the controller / processor 490 and I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of memory 495 and controller / processor 490 may be in wireless or wired communication with flow cytometer 410. In conjunction with memory 495 and I / O 497, controller / processor 490 may be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0172] The system illustrated in FIG. 4 includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for any detector), as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands 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 fluorescence 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 generally true that a portion of that label's emission spectrum also overlaps with the filter windows of one or more other detectors. This may be referred to as spillover. I / O 497 may be configured to receive data related to 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 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. Controller / processor 490 may be configured to evaluate one or more assignments of labels to markers.

[0173] FIG. 5 shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 500 is a flow system. The particle analysis system 500 shown in FIG. 5 can be configured to perform aspects of the methods described herein. The particle analysis system 500 includes a fluidics system 502. The fluidics system 502 can include or be coupled to a sample tube 510 and a moving fluid column within the sample tube through which particles 530 (e.g., cells) of the sample move along a common sample path 520.

[0174] The particle analysis system 500 includes a detection system 504 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection stations 508 generally refer to monitoring areas 540 of the common sample path. In some embodiments, detection may include detecting light, or one or more other characteristics, of particles 530 as they pass through the monitoring area 540. In FIG. 5, one detection station 508 is shown having one monitoring area 540. Some embodiments of the particle analysis system 500 may include multiple detection stations. Additionally, some detection stations may monitor more than one region.

[0175] Each signal is assigned a signal value to form a data point for each particle. As described above, this data may be referred to as event data. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 504 is configured to collect a series of such data points at a first time interval.

[0176] The particle analysis system 500 may also include a control system 506. The control system 506 may include one or more general-purpose processors, multiple parallel processing units, amplitude control circuitry 626, and / or frequency control circuitry 624, as shown in FIG. 6A and discussed below. The illustrated control system 506 may be operatively associated with the fluidics system 502. The control system 506 may be configured to generate a calculated signal frequency for at least a portion of a first time period based on the Poisson distribution and the number of data points collected by the detection system 504 during the first time period. The control system 506 may further be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time period. The control system 506 may additionally compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency. In addition, the control system 506 may be configured to generate a histogram representation of the cytometric data by encoding the histogram. The control system 506 can generate a representation of the histogram by using multiple parallel processing units to substantially simultaneously assign a color to each histogram value and then replicating the color encoding corresponding to the histogram values, for example, using a general-purpose processor.

[0177] FIG. 6A is a schematic diagram of a particle analyzer and sorter system 600 (e.g., particle analyzer 302 as shown in FIG. 3) according to one embodiment described herein. In some embodiments, particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, include, or be a nozzle 603. Within fluid conduit 601, sheath fluid 604 hydrodynamically focuses sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within moving fluid column 608, particles 609 (e.g., cells) move single-file across monitoring area 611 (e.g., where laser streams intersect) and are illuminated by illumination source 612 (e.g., a laser). Vibration of droplet forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610, some of which contain particles 609.

[0178] During operation, the detection station 614 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring area 611. The detection station 614 feeds a timing circuit 628, which in turn feeds a flash charging circuit 630. At the droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge is applied to the moving fluid column 608, causing the droplets of interest to carry a charge. The droplets of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating deflection plates (not shown) to deflect the droplets into a container, such as a collection tube or a multi-well or microwell sample plate, where a compartment, e.g., a compartment or well or microwell, can be associated with a particular droplet of interest. As shown in FIG. 6A, the droplets can be collected in a waste container 638.

[0179] Detection system 616 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through monitoring area 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. Detection system 616 enables the instrument to accurately calculate the position of each detected particle within the droplet. Detection system 616 can provide inputs to amplitude signal 620 and / or phase 618 signals, which in turn provide inputs to amplitude control circuit 626 and / or frequency control circuit 624 (via amplifier 622). Amplitude control circuit 626 and / or frequency control circuit 624 then control droplet forming transducer 602. Amplitude control circuit 626 and / or frequency control circuit 624 can be included within a control system.

[0180] In some embodiments, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store the detected events and sorting decisions based thereon. The sorting decisions can be included in the event data for the particles. In some embodiments, detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by one of detection system 616 or detection station 614 and provided to a non-collection element.

[0181] FIG. 6B is a schematic diagram of a particle analyzer and sorter system according to one embodiment presented herein. The particle analyzer and sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via stream charging wires within the barbs. This creates a stream of droplets 610 containing particles 610 for analysis. These particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel the charged droplets and direct them toward a destination collection vessel, such as a compartment (e.g., one of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a container 674 or along a second path 668 toward a container 678. If a particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sort range), the deflector plates can allow the particle to continue along flow path 664. Such uncharged droplets can be diverted into a waste container, such as via an aspirator 670.

[0182] Sorting electronics can be included to initiate measurement collection, receive fluorescent signals for particles, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 6B is the BD FACSAria® system of flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0183] In some embodiments, particles can be analyzed and characterized using one or more components described for particle sorter and separator system 600, regardless of whether the particles are physically sorted into a collection container. Similarly, particles can be analyzed and characterized using one or more components described for particle analysis system 500 (FIG. 5), regardless of whether the particles are physically sorted into a collection container. For example, particles can be grouped or displayed in a tree containing at least three groups, as described herein, or alternatively, displayed in one or more histogram formats using one or more of the components of particle sorter system 600 or particle analysis system 500.

[0184] Systems according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes at least one general-purpose processor and multiple parallel processing units, all of which access memory having stored instructions for performing the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, cache memory, data backup units, and many other devices. Each of the general-purpose processor and parallel processing units may be a commercially available processor or one of other processors that are or become available. The processor runs an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates 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, Python, R, Go, JavaScript, .NET, CUDA, Verilog, C++, other high-level languages, or low-level languages, and combinations thereof, as is known in the art. The operating system typically cooperates with the processor to coordinate and execute 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, one or more general-purpose processors and parallel processing units include analog electronics that provide feedback control, such as, for example, negative feedback control.

[0185] 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, flash memory devices, 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, tape drive, removable hard disk drive, or 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, magnetic tape, removable hard disk, or magnetic 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 system memory and / or program storage devices used in conjunction with memory storage devices.

[0186] In some embodiments, a computer program product is described comprising a computer-usable medium having control logic (computer software program including program code) stored therein. The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementation of hardware state machines to perform the functions described herein will be apparent to those skilled in the relevant art.

[0187] The memory may be any suitable device from which one or more general-purpose processors, as well as multiple parallel processing units such as graphics processors, can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks, or tape, or RAM, or any other suitable device, whether fixed or portable). A general-purpose processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. A parallel processing unit may include one or more graphics processors suitably programmed from a computer-readable medium carrying the necessary program code. Programming may be provided remotely to the processor via one or more communication channels, or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium, using any of these devices in conjunction with memory. For example, a magnetic or optical disk may carry the programming and be readable by a disk writer / reader. The system of the present invention also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. Programming according to the present invention may be recorded on a computer-readable medium, e.g., 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 magnetic 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.

[0188] The one or more general-purpose processors 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 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 mobile phone (i.e., a smartphone).

[0189] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0190] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., physical ports or interfaces 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.

[0191] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, thereby enabling the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that a user may use in conjunction with.

[0192] 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.

[0193] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communications interface using a common standard, such as, for example, 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, 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 touchscreen.

[0194] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., within the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0195] 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 pixels. 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 achieved 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. Presentation of data by the output manager may be implemented in accordance with various 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. Alternatively, 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 wireless systems, either networked or not. They may be co-located or physically separated.In some cases, various operating systems may be employed on any of the computer platforms, depending on the type and / or configuration of the computer platform selected. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Oracle Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, etc.

[0196] FIG. 7 illustrates the general architecture of an exemplary computing device 700 according to certain embodiments. The general architecture of computing device 700 illustrated in FIG. 7 includes an arrangement of computer hardware and software components. Computing device 700 may include more (or fewer) elements than those illustrated in FIG. 7 , although not all of these typically conventional elements need be shown to provide a useful disclosure. As illustrated, computing device 700 includes a processing unit 710, a network interface 720, a computer-readable medium drive 730, an input / output device interface 740, a display 750, and input devices 760, all of which can communicate with each other via a communications bus. Network interface 720 can provide connectivity to one or more networks or computing systems. Thus, processing unit 710 can receive information and instructions from other computing systems or services via a network. Processing unit 710 can also communicate with memory 770 and can further provide output information for optional display 750 via input / output device interface 740. The input / output device interface 740 may also receive input from optional input devices 760, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0197] Memory 770 may include computer program instructions (in some embodiments, grouped as modules or components) that processing unit 710 executes in sequence to implement one or more embodiments. Memory 770 typically includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 770 may store an operating system 772 that provides computer program instructions for use by processing unit 710 in the general management and operation of computing device 700. Memory 770 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0198] For example, in one embodiment, memory 770 includes a parameter processing module 774 for identifying a parameter of interest and / or for specifying positive and negative measurement intervals for the parameter of interest, and a scaling module 776 for scaling the cytometric data by converting the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0199] Suitable flow cytometry systems include, but are not limited to, 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 Thromb 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 certain instances, the flow cytometry systems of interest are a BD Biosciences FACSCanto™ flow cytometer, a BD Biosciences FACSCanto™ II flow cytometer, a BD Accuri™ flow cytometer, a BD Accuri™ C6 Plus flow cytometer, a BD Biosciences FACSCelesta™ flow cytometer, a BD Biosciences FACSLyric™ flow cytometer, a BD Biosciences FACSVerse™ flow cytometer, a BD Biosciences FACSymphony™ flow cytometer, a BD Biosciences LSRFortessa™ flow cytometer, a BD Biosciences LSRFortessa™ X-20 flow cytometer, a BD Biosciences FACSPresto™ flow cytometer, a BD Biosciences FACSVia™ flow cytometer, and a BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, a BD Biosciences These include the FACSLyric™ cell sorter, BD Biosciences 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, etc.

[0200] In some embodiments, the subject systems may be implemented using the same or similar technology as described in, for example, U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302 ,545, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. 9,726,527, No. 9,453,789, No. 9,200,334, No. 9,097,640, No. 9,095,494, No. 9,092,03 No. 4, No. 8,975,595, No. 8,753,573, No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,20 No. 1,875, No. 7,129,505, No. 6,821,740, No. 6,813,017, No. 6,809,804, No. 6,372,506, No. 5 ,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766, the disclosures of which are incorporated herein by reference in their entireties.

[0201] Computer-readable storage medium for scaling cytometric data Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for practicing the subject methods. The computer-readable storage medium may be employed on one or more computers for fully or partially automating systems for practicing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," in which case 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 magnetic disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blue-ray disks, solid-state disks, and network-attached storage devices (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording the information so that it can be later accessed and retrieved by a computer. The computer-implemented methods described herein may be performed using programming that can be written in one or more of any number of computer programming languages, including, 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.

[0202] In some embodiments, a subject computer-readable storage medium includes a computer program stored thereon, the computer program having instructions, when loaded into a computer, including an algorithm for obtaining cytometric data including measurements of multiple parameters from particles irradiated in a sample flowing in a flow stream, an algorithm for identifying a parameter of interest, an algorithm for specifying positive and negative measurement intervals for the parameter of interest, and an algorithm for scaling the cytometric data by converting the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0203] In embodiments, a subject computer-readable storage medium can be configured such that an algorithm for acquiring cytometric data includes receiving an input identifying a parameter of interest. In other embodiments, a subject computer-readable storage medium can be configured such that an algorithm for designating positive and negative measurement intervals for the parameter of interest includes receiving as input at least one of positive and negative measurement intervals for the parameter of interest.

[0204] In embodiments, the subject computer-readable storage medium may be configured such that the algorithm for scaling the cytometric data by transforming the parameter of interest includes rescaling a specified negative measurement interval of the parameter of interest. In some embodiments, rescaling the specified negative measurement interval of the parameter of interest includes reducing the standard deviation of the specified negative measurement interval of the parameter of interest. In other embodiments, the algorithm for scaling the cytometric data by transforming the parameter of interest includes rescaling a specified positive measurement interval of the parameter of interest. In some cases, rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size. In other cases, the predetermined size is the size of the negative measurement interval. In still other cases, the predetermined size is the size of a scaled positive measurement interval corresponding to a second parameter of the plurality of parameters.

[0205] In embodiments, the subject computer-readable storage medium may be configured such that the algorithm for scaling the cytometric data by transforming the parameter of interest includes adaptively scaling the parameter of interest according to:

[0206]

number

[0207] where s(x) represents the adaptively scaled measure of the parameter of interest, x represents the unscaled measure of the parameter of interest, and (n - ,n + ) is the specified negative measurement interval of the parameter, and (n + , p) is the specified positive measurement interval of the parameter, c is the compression ratio, X is the median of the negative measurement interval, and SD is the standard deviation of the negative measurement interval, calculated according to the following formula, where IQR is the interquartile range of the negative measurement interval:

[0208]

number

[0209] z(x) is the z-transform according to

[0210]

number

[0211] g(z) is the inverse hyperbolic sine function according to

[0212]

number

[0213]

number

[0214] is μ=z(n + ) and σ=1. In an embodiment, the default value of the compression ratio c is 70.

[0215] In embodiments, the subject computer-readable storage medium may further include an algorithm for displaying the scaled cytometric data on a display device. In such embodiments, the subject computer-readable storage medium may be configured such that the algorithm for displaying the scaled cytometric data on a display device includes displaying a plot of the cytometric data including the transformed parameter of interest.

[0216] In certain embodiments, the algorithm for designating positive and negative measurement intervals for the parameter of interest includes using one-dimensional gating to designate at least one of the positive and negative measurement intervals for the parameter of interest. In other embodiments, the algorithm for designating positive and negative measurement intervals for the parameter of interest includes receiving as input a one-dimensional gate designating the positive and negative measurement intervals for the parameter of interest. In yet other embodiments, the algorithm for designating positive and negative measurement intervals for the parameter of interest includes applying a fluorescence minus one control to designate at least one of the positive and negative measurement intervals for the parameter of interest. In yet other embodiments, the algorithm for designating positive and negative measurement intervals for the parameter of interest includes applying a mathematical model to designate at least one of the positive and negative measurement intervals for the parameter of interest. In yet other embodiments, the algorithm for designating positive and negative measurement intervals for the parameter of interest includes applying a machine learning algorithm to designate one or both of the positive and negative measurement intervals for the parameter of interest.

[0217] In some cases, the subject computer-readable storage medium may be configured such that the instructions further include an algorithm for identifying one or more additional parameters of interest, an algorithm for specifying positive and negative measurement intervals for each additional parameter of interest, and an algorithm for scaling the cytometric data by converting each additional parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals.

[0218] In some cases, the subject computer-readable storage medium may be configured such that the instructions further include an algorithm for clustering the scaled cytometric data by applying a clustering algorithm to the scaled cytometric data. In particular cases, the algorithm for displaying the scaled cytometric data on a display device includes causing the display of clusters of the scaled cytometric data.

[0219] In embodiments of the subject non-transitory computer-readable storage medium, the cytometric data is high-dimensional data. In some cases, the plurality of measurement parameters ranges from 2 to about 300,000 measurement parameters.

[0220] The computer-readable storage medium may 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 will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates 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 is known in the art. The operating system typically cooperates with the processor to coordinate and execute 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.

[0221] Usability The subject systems, methods, and computer systems find application in a variety of fields where it is desirable to identify, analyze, and in some cases, sort particulate components, such as cells, within a sample in a fluid medium, such as a biological sample. In some embodiments, the systems and methods described herein find application in flow cytometric characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods find application in emitted light spectroscopy. Additionally, the subject systems and methods find application in analyzing samples, such as by reducing the impact of noise on collected data or improving the effectiveness of clustering algorithms. As a result, in some cases, the subject systems and methods can find use in distinguishing between different particle types within a sample, such as different cell types within a biological sample. Furthermore, the subject systems and methods find application in improving the efficiency and effectiveness of sorting samples (e.g., in a flow stream). Improving the efficiency of sorting a sample means that when the subject systems and methods are used, particles, such as cells, of the sample are less likely to be misidentified or misinterpreted when sorting or analyzing the sample, e.g., due to measurement noise that is indistinguishable from a signal. In particular, the subject systems and methods may improve the efficiency and effectiveness of analysis or sorting when high-dimensional data is collected and analyzed. Embodiments of the present disclosure find use during cell sorting where it is desirable to provide a flow cytometer with improved cell sorting efficiency, increased particle collection, particle charging efficiency, or more accurate particle charging.

[0222] Embodiments of the present disclosure also find use in applications where cells prepared from a biological sample may be desirable for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate the identification and / or acquisition of individual cells or populations thereof prepared from a target fluid or tissue biological sample. For example, the subject methods and systems may facilitate the identification and / or acquisition of cells from fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate the identification and / or acquisition of cells from fluid or tissue samples used in therapeutics. The disclosed methods and devices enable the analysis and / or isolation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with increased efficiency and effectiveness and at low cost compared to conventional flow cytometry systems, especially when high-dimensional data is collected and / or analyzed.

[0223] The following are offered by way of example and not by way of limitation.

[0224] experiment 8 shows a two-dimensional plot 800A illustrating two parameters of cytometric data scaled according to a default scaling approach. In contrast, plot 800B illustrates the same two parameters, but this time scaled according to the subject method. That is, the only difference between the representation of the data in plots 800A and 800B is how the data containing the two parameters is scaled.

[0225] Figure 9 shows a two-dimensional plot 900A generated based on 1,000 iterations of the opt-SNE algorithm, as implemented in FlowJo 10.7, applied to 12-parameter PBMC (peripheral blood mononuclear cell) cytometric data scaled according to the default scaling approach also implemented in FlowJo 10.7 software. Because the underlying data represent measurements of PBMCs, the color coding in plots 900A and 900B represents local cell density, with darker / blue shades representing areas of lower cell density and greener / lighter shades representing areas of higher cell density. In contrast, plot 900B shows the same cytometric data, but this time scaled according to the subject method. Upon visual inspection of plot 900B of data scaled according to the subject method, compared to plot 900A, it is clear that the opt-SNE algorithm identifies more clearly defined groups, or clusters, with finer structure to the data.

[0226] Figure 10 shows the same opt-SNE plots as seen in Figures 9A and 9B, corresponding to Figures 9A and 9B, respectively, for the same cytometric data, but with cells colored or shaded according to cluster identity. Clusters were identified by the X-shift clustering algorithm. Application of the X-shift clustering algorithm identified 34 clusters in a dataset scaled according to the default (state-of-the-art) scaling method, compared to 82 clusters in a dataset scaled according to the subject method. Thus, scaling cytometric data according to the subject method results in both the X-shift and opt-SNE algorithms detecting significantly more populations. These findings demonstrate that the subject scaling method results in an unexpected and dramatic improvement in the sensitivity of population identification by multidimensional analysis algorithms such as opt-SNE and X-shift. That is, scaling data according to the subject method unexpectedly caused the same clustering algorithm to identify significantly more clusters with the same cytometric data, i.e., the same sample and data collection technique.

[0227] In each of Figures 8, 9 and 10, the cytometric data presented is a 12-color PBMC data set collected on a FACSLyric cytometer instrument and subjected to spillover compensation according to standard operating procedures.

[0228] Regardless of the scope of the appended claims, the present disclosure is also defined by the following notes.

[0229] 1. A method for scaling cytometric data, comprising: acquiring cytometric data of the sample comprising measurements of a plurality of parameters from illuminated particles in the sample flowing within the flow stream; Identifying a parameter of interest; specifying positive and negative measurement intervals for the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals; A method comprising: 2. The method of claim 1, wherein transforming the parameter of interest includes rescaling a specified negative measurement interval of the parameter of interest. 3. The method of claim 2, wherein rescaling the specified negative measurement interval of the parameter of interest comprises reducing the standard deviation of the specified negative measurement interval of the parameter of interest. 4. The method of claim 2 or 3, wherein transforming the parameter of interest further comprises rescaling the specified positive measurement interval of the parameter of interest. 5. The method of claim 4, wherein rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size.

[0230] 6. The method of claim 5, wherein the predetermined size is the size of the negative measurement interval. 7. The method of claim 5, wherein the predetermined size is the size of a scaled positive measurement interval corresponding to a second parameter of the plurality of parameters. 8. Transforming the parameter of interest includes adaptively scaling the parameter of interest according to:

[0231]

number

[0232] During the ceremony, s(x) represents the adaptively scaled measure of the parameter of interest; x represents the unscaled measure of the parameter of interest, (n - ,n + ) is the specified negative measurement interval of the parameter of interest, (n + , p) is the specified positive measurement interval of the parameter of interest; c is the compression ratio, Bar X is the median of the negative measurement interval, SD is the standard deviation of the negative measurement interval and is calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval;

[0233]

number

[0234] z(x) is the z-transform according to

[0235]

number

[0236] g(z) is the inverse hyperbolic sine function according to

[0237]

number

[0238]

number

[0239] is μ=z(n + ) and σ=1, which is the cumulative distribution function of the standard normal distribution. The method according to any one of Appendices 1 to 7. 9. The method of claim 8, wherein the default value of the compression ratio c is 70. 10. The method of any one of claims 1 to 9, further comprising displaying the scaled cytometric data.

[0240] 11. The method of claim 10, wherein displaying the scaled cytometric data includes displaying a plot of the cytometric data including the transformed parameter of interest. 12. A method according to any one of appendices 1 to 11, wherein specifying at least one of positive and negative measurement intervals for the parameter of interest includes performing one-dimensional gating to specify the measurement interval. 13. The method of any one of appendices 1 to 11, wherein specifying at least one of positive and negative measurement intervals for the parameter of interest includes applying a fluorescence minus one control to specify the measurement interval. 14. The method of any one of appendices 1 to 11, wherein specifying at least one of positive and negative measurement intervals for the parameter of interest includes applying a mathematical model to specify the measurement interval. 15. The method of any one of appendices 1 to 11, wherein specifying one or both of positive and negative measurement intervals for the parameter of interest includes applying a machine learning algorithm to specify the measurement intervals.

[0241] 16. Identifying one or more additional parameters of interest; specifying positive and negative measurement intervals for each additional parameter of interest; scaling the cytometric data by transforming each additional parameter of interest based at least in part on corresponding specified positive and negative measurement intervals; 16. The method according to any one of claims 1 to 15, further comprising: 17. The method of claim 16, wherein the specified positive measurement intervals of each parameter of interest are rescaled to the same predetermined size. 18. The method of any one of claims 1 to 17, further comprising clustering the cytometric data by applying a clustering algorithm to the scaled cytometric data. 19. The method of claim 18, further comprising displaying the scaled cytometric data by displaying clusters of the scaled cytometric data. 20. The method of any one of appendices 1 to 19, wherein the scaled cytometric data is used to improve the performance of a clustering algorithm applied to the cytometric data.

[0242] 21. The method of any one of claims 1 to 20, wherein scaled cytometric data is used to reduce the effects of measurement noise. 22. The method of any one of appendices 1 to 21, wherein the particles are cells. 23. The method of claim 22, wherein the scaled cytometric data is used to distinguish between two similar cell populations. 24. The method of any one of appendices 1 to 23, wherein the cytometric data is high-dimensional data. 25. The method of any one of appendices 1 to 24, wherein the plurality of measurement parameters ranges from 20,000 to about 300,000 measurement parameters.

[0243] 26. The method of any one of claims 1 to 25, wherein the cytometric data comprises light measurements from illuminated particles in the sample. 27. The method of claim 26, wherein the light measurement is a measurement of light intensity. 28. Cytometric data is excitation light scattered by the particle along approximately the forward direction; excitation light scattered by the particle along a generally lateral direction, and Light emitted from fluorescent molecules or dyes used to label particles in one or more frequency ranges 28. The method of claim 26 or 27, comprising measuring one or more of: 29. The method of any one of claims 1 to 28, wherein obtaining cytometric data for the sample comprises obtaining measurements from flow cytometric analysis of the sample.

[0244] 30. A method of analyzing scaled cytometric data, comprising: acquiring cytometric data of the sample comprising measurements of a plurality of parameters from illuminated particles in the sample flowing within the flow stream; Identifying a parameter of interest; For one or more measurements of the parameter of interest each corresponding to one or more particles, Calculating the probability that a measurement exhibits a particular characteristic; and scaling the cytometric data by differentially transforming the measurements based on the probability that the measurements exhibit a particular characteristic; analyzing the scaled cytometric data with at least a first data analysis algorithm; A method comprising: 31. The method of claim 30, wherein the positive measurement interval is designated based on the probability that the measurement exhibits a particular characteristic for one or more particles. 32. The method of claim 31, further comprising rescaling the positive measurement interval of the parameter of interest based on the size of the scaled positive measurement interval corresponding to a second parameter of the plurality of parameters. 33. The method of claim 31 or 32, further comprising specifying a negative measurement interval. 34. The method of claim 33, wherein the probability that a measurement does not exhibit a particular characteristic is determined based on whether the measurement falls within a negative measurement interval.

[0245] 35. Transforming the measurements includes adaptively scaling the measurements according to:

[0246]

number

[0247] During the ceremony, s(x) represents the adaptively scaled measure of the parameter of interest; x represents the unscaled measure of the parameter of interest, (n - ,n +) is the negative measurement interval of the parameter of interest, (n + , p) is the positive measurement interval of the parameter of interest, c is the compression ratio, Bar X is the median of the negative measurement interval, SD is the standard deviation of the negative measurement interval and is calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval;

[0248]

number

[0249] z(x) is the z-transform according to

[0250]

number

[0251] g(z) is the inverse hyperbolic sine function according to

[0252]

number

[0253]

number

[0254] is μ=z(n + ) and σ=1, which is the cumulative distribution function of the standard normal distribution. 35. The method according to any one of appendices 30 to 34. 36. The method of any one of claims 29-35, further comprising analyzing one or more transformed measurements using one or more additional data analysis algorithms. 37. The method of any one of appendices 30-36, wherein one or more of the first data analysis algorithm or the additional analysis algorithm comprises a clustering algorithm or a dimensionality reduction algorithm.

[0255] 38. A system for scaling cytometric data, comprising: an apparatus configured to acquire cytometric data comprising measurements of a plurality of parameters from illuminated particles in a sample flowing within the flow stream; a processor including a memory operatively coupled thereto; It is equipped with The memory has instructions stored therein that, when executed by the processor, cause the processor to: Identifying a parameter of interest; specifying positive and negative measurement intervals for the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on the corresponding specified positive and negative measurement intervals; A system that allows the following to be performed. 39. The system of claim 38, wherein the processor is configured to receive an input identifying a parameter of interest. 40. The system of claim 38 or 39, wherein the processor is configured to receive as input at least one of a positive and a negative measurement interval for the parameter of interest. 41. The system of any one of appendices 38-40, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to transform the parameter of interest by rescaling a specified negative measurement interval of the parameter of interest. 42. The system of claim 41, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to rescale the specified negative measurement interval of the parameter of interest by decreasing the standard deviation of the specified negative measurement interval of the parameter of interest.

[0256] 43. The system of claim 41 or 42, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to further transform the parameter of interest by rescaling a specified positive measurement interval of the parameter of interest. 44. The system of claim 43, wherein rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size. 45. The system of claim 44, wherein the predetermined size is the size of a scaled positive measurement interval corresponding to a second parameter of the plurality of parameters. 46. ​​The memory has further instructions stored in the memory that, when executed by the processor, cause the processor to transform the parameter of interest by adaptively scaling the parameter of interest according to:

[0257]

number

[0258] During the ceremony, s(x) represents the adaptively scaled measure of the parameter of interest; x represents the unscaled measure of the parameter of interest, (n - ,n + ) is the specified negative measurement interval of the parameter of interest, (n + ,p) is the specified positive measurement interval of the parameter, c is the compression ratio, Bar X is the median of the negative measurement interval, SD is the standard deviation of the negative measurement interval and is calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval;

[0259]

number

[0260] z(x) is the z-transform according to

[0261]

number

[0262] g(z) is the inverse hyperbolic sine function according to

[0263]

number

[0264]

number

[0265] is μ=z(n + ) and σ=1, which is the cumulative distribution function of the standard normal distribution. 46. ​​A system according to any one of appendices 38 to 45. 47. The system of claim 46, wherein the memory has further instructions stored in the memory, the instructions, when executed by the processor, cause the processor to use a default value 70 for the compression ratio c.

[0266] 48. A system described in any one of appendices 38 to 47, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to display the scaled cytometric data on a display device. 49. The system of claim 48, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to display the scaled cytometric data on a display device by causing a plot of the cytometric data including the transformed parameter of interest to be displayed. 50. The system of any one of appendices 38 to 49, wherein the memory includes further instructions stored in the memory, which, when executed by the processor, cause the processor to use one-dimensional gating to specify at least one of positive and negative measurement intervals for the parameter of interest. 51. The system of claim 50, wherein the processor is configured to receive as input a one-dimensional gate specifying positive and negative measurement intervals for the parameter of interest. 52. A system described in any one of appendices 38 to 50, wherein the memory has further instructions stored in the memory, which, when executed by the processor, cause the processor to apply a fluorescence minus one control to specify at least one of a positive and a negative measurement interval for the parameter of interest.

[0267] 53. The system of any one of appendices 38 to 50, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to apply a mathematical model to specify at least one of positive and negative measurement intervals for the parameter of interest. 54. A system described in any one of appendices 38 to 50, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to apply a machine learning algorithm to specify one or both of positive and negative measurement intervals for the parameter of interest. 55. The memory has further instructions stored in the memory, the instructions, when executed by the processor, causing the processor to: identifying one or more additional parameters of interest; specifying positive and negative measurement intervals for each additional parameter of interest; scaling the cytometric data by transforming each additional parameter of interest based at least in part on corresponding specified positive and negative measurement intervals; The system according to any one of appendices 38 to 54, 56. The system of claim 55, wherein the specified positive measurement intervals of each parameter of interest are rescaled to the same predetermined size. 57. The system of any one of notes 38 to 56, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to cluster the cytometric data by applying a clustering algorithm to the scaled cytometric data.

[0268] 58. The system of claim 57, wherein the memory has further instructions stored in the memory that, when executed by the processor, cause the processor to display clusters of scaled cytometric data. 59. A system according to any one of appendices 38 to 58, wherein the system is configured to scale the cytometric data so as to improve the performance of a clustering algorithm applied to the cytometric data. 60. A system described in any one of appendices 38 to 59, wherein the system is configured to scale the cytometric data so that the effects of measurement noise are reduced. 61. A system described in any one of Appendices 38 to 60, wherein the particles are cells. 62. The system of claim 61, wherein the system is configured to distinguish between two similar cell populations based on the scaled cytometric data.

[0269] 63. A system according to any one of appendices 38 to 62, wherein the cytometric data is high-dimensional data. 64. The system of any one of Appendices 38 to 63, wherein the plurality of measurement parameters ranges from 20,000 to approximately 300,000 measurement parameters. 65. A system according to any one of claims 38 to 64, wherein the cytometric data includes light measurements from illuminated particles in the sample. 66. The system of claim 65, wherein the light measurement is a measurement of light intensity. 67. Cytometric data excitation light scattered by the particle along approximately the forward direction; excitation light scattered by the particle along a generally lateral direction, and Light emitted from fluorescent molecules or dyes used to label particles in one or more frequency ranges 67. The system of claim 65 or 66, comprising one or more measurements of: 68. A system described in any one of appendices 38 to 67, wherein the cytometric data of the sample includes measurements obtained from a flow cytometer configured to analyze the sample.

[0270] 69. A non-transitory computer-readable storage medium, comprising: having instructions stored on a non-transitory computer-readable storage medium for scaling cytometric data; The command, an algorithm for acquiring cytometric data comprising measurements of a plurality of parameters from illuminated particles in a sample flowing in the flow stream; an algorithm for identifying a parameter of interest; an algorithm for specifying positive and negative measurement intervals for the parameter of interest; an algorithm for scaling the cytometric data by transforming the parameter of interest based at least in part on corresponding specified positive and negative measurement intervals; 1. A non-transitory computer-readable storage medium comprising: 70. The non-transitory computer-readable storage medium of claim 69, wherein the algorithm for obtaining cytometric data includes receiving an input identifying a parameter of interest. 71. The non-transitory computer-readable storage medium of claim 69 or 70, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes receiving as input at least one of positive and negative measurement intervals for the parameter of interest. 72. A non-transitory computer-readable storage medium according to any one of appendices 69 to 71, wherein the algorithm for scaling cytometric data by transforming a parameter of interest includes rescaling specified negative measurement intervals of the parameter of interest. 73. The non-transitory computer-readable storage medium of claim 72, wherein rescaling the specified negative measurement interval of the parameter of interest includes reducing the standard deviation of the specified negative measurement interval of the parameter of interest.

[0271] 74. The non-transitory computer-readable storage medium of claim 72 or 73, wherein the algorithm for scaling cytometric data by transforming the parameter of interest includes rescaling a specified positive measurement interval of the parameter of interest. 75. The non-transitory computer-readable storage medium of claim 74, wherein rescaling the specified positive measurement interval includes rescaling the positive measurement interval to a predetermined size. 76. The non-transitory computer-readable storage medium of claim 75, wherein the predetermined size is the size of a scaled positive measurement interval corresponding to a second parameter of the plurality of parameters. 77. An algorithm for scaling cytometric data by transforming a parameter of interest includes adaptively scaling the parameter of interest according to:

[0272]

number

[0273] During the ceremony, s(x) represents the adaptively scaled measure of the parameter of interest; x represents the unscaled measure of the parameter of interest, (n - ,n + ) is the specified negative measurement interval of the parameter of interest, (n +,p) is the specified positive measurement interval of the parameter, c is the compression ratio, Bar X is the median of the negative measurement interval, SD is the standard deviation of the negative measurement interval and is calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval;

[0274]

number

[0275] z(x) is the z-transform according to

[0276]

number

[0277] g(z) is the inverse hyperbolic sine function according to

[0278]

number

[0279]

number

[0280] is μ=z(n + ) and σ=1, which is the cumulative distribution function of the standard normal distribution. 77. The non-transitory computer-readable storage medium of any one of claims 69 to 76. 78. The non-transitory computer-readable storage medium of claim 77, wherein the default value of the compression ratio c is 70.

[0281] 79. The non-transitory computer-readable storage medium of any one of Clauses 69-78, further comprising an algorithm for displaying the scaled cytometric data on a display device. 80. The non-transitory computer-readable storage medium of claim 79, wherein the algorithm for displaying the scaled cytometric data on a display device includes displaying a plot of the cytometric data including the transformed parameter of interest. 81. A non-transitory computer-readable storage medium described in any one of appendices 69 to 80, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes using one-dimensional gating to specify at least one of positive and negative measurement intervals for the parameter of interest. 82. The non-transitory computer-readable storage medium of claim 81, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes receiving as input a one-dimensional gate that specifies positive and negative measurement intervals for the parameter of interest. 83. A non-transitory computer-readable storage medium described in any one of appendices 69 to 80, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes applying a fluorescence minus one control to specify at least one of positive and negative measurement intervals for the parameter of interest.

[0282] 84. A non-transitory computer-readable storage medium described in any one of appendices 69 to 80, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes applying a mathematical model to specify at least one of positive and negative measurement intervals for the parameter of interest. 85. A non-transitory computer-readable storage medium described in any one of appendices 69 to 80, wherein the algorithm for specifying positive and negative measurement intervals for the parameter of interest includes applying a machine learning algorithm to specify one or both of the positive and negative measurement intervals for the parameter of interest. 86. The command is an algorithm for identifying one or more additional parameters of interest; an algorithm for specifying positive and negative measurement intervals for each additional parameter of interest; an algorithm for scaling the cytometric data by converting each additional parameter of interest based at least in part on corresponding specified positive and negative measurement intervals; 86. The non-transitory computer-readable storage medium of any one of claims 69 to 85, further comprising: 87. The non-transitory computer-readable storage medium of claim 86, wherein the specified positive measurement intervals of each parameter of interest are rescaled to the same predetermined size. 88. The non-transitory computer-readable storage medium of any one of appendices 69 to 87, wherein the instructions further comprise an algorithm for clustering the cytometric data by applying a clustering algorithm to the scaled cytometric data.

[0283] 89. The non-transitory computer-readable storage medium of claim 88, wherein the algorithm for displaying the scaled cytometric data on a display device includes causing the display of clusters of the scaled cytometric data. 90. The non-transitory computer-readable storage medium of any one of appendices 69 to 89, wherein the cytometric data is high-dimensional data. 91. The non-transitory computer-readable storage medium of any one of Appendices 69 to 90, wherein the plurality of measurement parameters ranges from 2 to approximately 300,000 measurement parameters. 92. The non-transitory computer-readable storage medium of any one of Appendices 69 to 91, wherein the cytometric data includes light measurements from illuminated particles in the sample. 93. The non-transitory computer-readable storage medium of claim 92, wherein the light measurements are measurements of light intensity.

[0284] 94. Cytometric data excitation light scattered by the particle along approximately the forward direction; excitation light scattered by the particle along a generally lateral direction, and Light emitted from fluorescent molecules or dyes used to label particles in one or more frequency ranges 94. The non-transitory computer-readable storage medium of claim 92 or 93, comprising one or more measurements of: 95. The non-transitory computer-readable storage medium of any one of claims 69 to 94, wherein the cytometric data of the sample includes measurements obtained from a flow cytometer configured to analyze the sample.

[0285] Although the foregoing invention has been described in some detail by way of illustration and example, for purposes of clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of the invention that certain changes and modifications can be made thereto without departing from the spirit or scope of the appended claims.

[0286] Accordingly, the foregoing description merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various arrangements, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present invention and concepts provided by the inventors to further advance the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein describing principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, 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 as a public dedication, regardless of whether such disclosure is expressly recited in the claims.

[0287] Accordingly, 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. For the purposes of the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) are expressly defined to apply to a limitation in a claim only if the exact phrase "means for" or the exact phrase "step for" appears at the beginning of such limitation in the claim. If such exact phrases are not used in a limitation in a claim, then neither 35 U.S.C. 112(f) nor 35 U.S.C. 112(6) applies.

[0288] cross reference Pursuant to 35 U.S.C. §119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 115,994, filed November 19, 2020, the entire disclosure of which is incorporated herein by reference.

Claims

1. 1. A method for scaling cytometric data, comprising: acquiring cytometric data of the sample comprising measurements of a plurality of parameters from illuminated particles in the sample flowing within the flow stream; Identifying a parameter of interest; specifying positive and negative measurement intervals for the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on a corresponding designated positive measurement interval and by transforming the parameter of interest based at least in part on a corresponding designated negative measurement interval; A method comprising:

2. The method of claim 1 , wherein transforming the parameter of interest comprises rescaling the specified negative measurement interval of the parameter of interest.

3. The method of claim 2 , wherein rescaling the specified negative measurement interval of the parameter of interest comprises decreasing the standard deviation of the specified negative measurement interval of the parameter of interest.

4. The method of claim 2 or 3, wherein transforming the parameter of interest further comprises rescaling a specified positive measurement interval of the parameter of interest.

5. The method of claim 4 , wherein rescaling the specified positive measurement interval comprises rescaling the positive measurement interval to a predetermined size.

6. The method of claim 5 , wherein the predetermined size is the size of the negative measurement interval.

7. The method of claim 5 , wherein the predetermined size is the size of a scaled positive measurement interval corresponding to a second parameter of the plurality of parameters.

8. Transforming the parameter of interest includes adaptively scaling the parameter of interest according to: [Equation 1] During the ceremony, s(x) represents an adaptively scaled measure of the parameter of interest; x represents an unscaled measurement of the parameter of interest; (n － , n ＋ ) is the specified negative measurement interval of the parameter of interest; (n ＋ , p) is a specified positive measurement interval of the parameter of interest; c is the compression ratio, X is the median of the negative measurement interval; SD is the standard deviation of the negative measurement interval, calculated according to the following formula: where IQR is the interquartile range of the negative measurement interval; [Equation 2] z(x) is the z-transform according to [Equation 3] g(z) is the inverse hyperbolic sine function according to [Equation 4] [Equation 5] μ = z(n ＋ ) and σ = 1, which is the cumulative distribution function of the standard normal distribution, The method according to any one of claims 1 to 7.

9. The method of any one of claims 1 to 8, further comprising displaying the scaled cytometric data.

10. 10. The method of claim 1, wherein specifying at least one of a positive and a negative measurement interval for the parameter of interest comprises performing one-dimensional gating to specify the measurement interval.

11. The method of any one of claims 1 to 10, further comprising clustering the scaled cytometric data by applying a clustering algorithm to the scaled cytometric data.

12. The method according to any one of claims 1 to 11, wherein the cytometric data is high-dimensional data.

13. The method of any one of claims 1 to 12, wherein obtaining cytometric data of a sample comprises obtaining measurements from flow cytometric analysis of the sample.

14. 1. A system for scaling cytometric data, comprising: an apparatus configured to acquire cytometric data comprising measurements of a plurality of parameters from illuminated particles in a sample flowing in a flow stream; a processor including a memory operatively coupled thereto; It is equipped with The memory has instructions stored therein that, when executed by the processor, cause the processor to: Identifying a parameter of interest; specifying positive and negative measurement intervals for the parameter of interest; scaling the cytometric data by transforming the parameter of interest based at least in part on a corresponding designated positive measurement interval and by transforming the parameter of interest based at least in part on a corresponding designated negative measurement interval; A system that allows the following to be performed.

15. 1. A non-transitory computer-readable storage medium, comprising: instructions stored on the non-transitory computer-readable storage medium for scaling cytometric data; The instruction: an algorithm for acquiring cytometric data comprising measurements of a plurality of parameters from illuminated particles in a sample flowing in the flow stream; an algorithm for identifying a parameter of interest; an algorithm for specifying positive and negative measurement intervals for the parameter of interest; an algorithm for scaling the cytometric data by transforming the parameter of interest based at least in part on a corresponding designated positive measurement interval and by transforming the parameter of interest based at least in part on a corresponding designated negative measurement interval; 1. A non-transitory computer-readable storage medium comprising:

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