Method and system for dimension reduction

Abstract

A computer-implemented dimensionality reduction method is provided. A method of interest includes receiving a secondary dataset including data points acquired from a secondary sample and calculating a second dimensional reduction of the secondary dataset based on a first dimensional reduction of a reference dataset including data points acquired from a reference sample. The calculated second dimensionality reduction is compatible with the dimensionality-reduced reference data set. Systems and non-transitory computer-readable storage media configured to perform the methods of the invention are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Serial No. 63 / 421,839, filed on November 2, 2022; the disclosure of which is incorporated herein by reference in its entirety. Background Art

[0003] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample. A flow cytometer typically includes a sample container for receiving a fluid sample (such as a blood sample) and a sheath fluid container containing a sheath fluid. The flow cytometer delivers particles (including cells) in the fluid sample to a flow cell in the form of a cell stream, while also directing the sheath fluid to the flow cell. To characterize the components of the flow stream, the flow stream is illuminated with light. Changes in the material in the flow stream (such as morphology or the presence of fluorescent markers) can cause changes in the observed light, and these changes can allow characterization and separation. For example, particles (such as molecules, analyte-bound beads, or individual cells) in a fluid suspension pass through a detection area where the particles are exposed to excitation light (usually from one or more lasers) and the light scattering and fluorescence properties of the particles are measured. The particles or their components are often labeled with fluorescent dyes to facilitate detection. By labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. In some embodiments, the analyzer includes multiple detectors, one for each scattering parameter to be measured, and one or more detectors for each different dye to be detected. For example, some embodiments include spectroscopic configurations in which more than one sensor or detector is used for each dye. The data obtained includes the signal and fluorescence emission measured for each light scattering detector.

[0004] The flow cytometer may also include a device for recording the measured data and analyzing the data. For example, a computer connected to the detection electronics may be used for data storage and analysis. For example, the data may be stored in a tabular format, where each row corresponds to the data for one particle and each column corresponds to each measured feature. Using a standard file format (such as the "FCS" file format) to store data from a particle analyzer facilitates analysis of the data using a separate program and / or machine. Using current analysis methods, data is typically displayed in the form of a one-dimensional histogram or a two-dimensional (2D) graph for ease of visualization, but other methods may also be used to visualize multidimensional data.

[0005] Parameters measured using, for example, a flow cytometer typically include: light of the excitation wavelength scattered by particles in a primarily forward direction at narrow angles (called forward scatter (FSC)); excitation light scattered by particles in a direction orthogonal to the excitation laser (called side scatter (SSC)); and light emitted from fluorescent molecules in one or more detectors (which measure signals across a range of spectral wavelengths), or light emitted by fluorescent dyes that are primarily detected in a specific detector or detector array. Different cell types can be identified by their light scattering properties and by fluorescence emissions, which are produced by labeling various cellular proteins or other components with antibodies or other fluorescent probes labeled with fluorescent dyes.

[0006] A common high-dimensional analysis workflow involves creating or deriving a set of parameters that represent cells in a low-dimensional image by creating a smaller set of parameters (most commonly two) that attempt to summarize information from all the other parameters. This is known as dimensionality reduction (dim redux). A typical dimensionality reduction workflow assumes that the user concatenates all input samples and then runs dimensionality reduction on the single concatenated dataset. Traditionally, users of flow cytometry that perform dimensionality reduction, such as tSNE, UMAP, EmbedSOM, TriMap, PacMAP, etc., have been forced to pre-concatenate the input samples because there is no general solution that allows them to compute embeddings for a sample based on previously computed embeddings of different samples. For example, Figure 1 Depicts a typical dimensionality reduction workflow in flow cytometry. Figure 1 As shown, data sets 101a-101c (e.g., in the format of .fcs files) from different samples are concatenated into a single file 102, based on which new parameters are derived 103. A common subsequent step is to use a clustering algorithm to partition the data into similar and dissimilar cell groups, which are generally consistent with phenotype, and store the cluster membership numbers as derived parameters.

[0007] These methods are often nondeterministic; they can produce different results if run multiple times. When the algorithm runs, cells with the same or similar phenotypes cluster together, but they don't always face the same direction, making direct comparison difficult. Identical phenotypes are also not always assigned the same cluster number. Traditionally, all data to be compared needs to be included in the parameter creation step, which means all the data must be processed at once. If additional datasets are generated, the entire process needs to be restarted. Summary of the Invention

[0008] The inventors have realized that traditional dimensionality reduction methods are not practical because they require all data to be collected in advance and the dimensionality reduction calculations to be performed on large input files. This poses challenges in terms of computing resources and computing time. In addition, adding new data to the analysis requires the entire analysis to be redone, which exacerbates the inefficiency. The concatenation step may also lose contact with the metadata, which then needs to be manually tracked. In addition, it has been found that there is no general solution that allows the user to calculate the embedding of a sample based on the embedding of a different sample calculated previously, rather than embedding the new sample into an existing dimensionality reduction method. Therefore, there is a need for an algorithmic technique that calculates dimensionality reduction for a new sample based on the existing dimensionality reduction of a different (i.e., reference) sample. The methods, systems, and computer-readable media of the present invention meet this need.

[0009] Aspects of the present invention include computer-implemented dimensionality reduction methods. Methods of interest include receiving a secondary dataset comprising data points collected from a secondary sample; and computing a second dimensionality reduction for the secondary dataset based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample. The second dimensionality reduction computed in the method is compatible with the reduced reference dataset. In some cases, the method includes determining the data points collected from the reference sample that are closest to each data point collected from the secondary sample with respect to one or more given data parameters. For example, computing the second dimensionality reduction may include computing k nearest neighbors within the reference dataset for each data point collected from the secondary sample. The value of k may vary and may range from 1 to m, where m is half the number of data points in the reference sample. In some cases, k ranges from 2 to 5. In embodiments, the method includes computing the k nearest neighbors using a vantage point tree, a k-dimensional tree, a sphere tree, a cover tree, locality-sensitive hashing, hierarchical navigable small-world, approximate nearest neighbors with random projection trees, a GPU-based KNN search, or a brute-force KNN search. In an embodiment, the method includes calculating the distance (e.g., Manhattan distance, Euclidean distance, Chebyshev distance, Minkowski distance, cosine distance) between each data point collected from the secondary sample and each of the k nearest neighbors. In some versions, the method includes calculating the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors. In other cases, such as when deriving classification parameters, the method includes calculating the second dimensionality reduction based on a weighted majority voting method. The method according to some embodiments includes calculating a weighted average using a weight obtained based on the distance for each of the k nearest neighbors. In some cases, the secondary dataset includes data points associated with multiple parameters matched to a reference dataset. The method according to some embodiments includes creating derived parameters for the secondary dataset based on parameters of the reduced reference dataset (e.g., fluorescence parameters, scattering parameters, imaging parameters, or classification parameters). In some cases, the derived parameters are fluorescence parameters. In selected versions, creating the derived parameters for the secondary dataset includes linear interpolation.

[0010] According to some embodiments, the method includes calculating a quality score designed to indicate whether the data is sufficiently stable over time. In some such cases, the method includes calculating an input quality score that indicates how closely each data point collected from the secondary sample is correlated with data points in a reference data set. Calculating the input quality score can include, for example, obtaining a normalized average distance from each data point collected from the secondary sample to its k nearest neighbors. In other cases, the method includes calculating an output quality score that measures the separation of the k nearest neighbors in the reduced first data set. Calculating the output quality score can include, for example, obtaining a normalized average distance for each of the k nearest neighbors in the reduced reference data set.

[0011] In some cases, the reference dataset and the secondary dataset include flow cytometer data. In selected versions, the reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset. In certain embodiments, the method includes receiving the reference dataset and performing a first dimensionality reduction. Methods according to some embodiments include transforming the reference dataset and the secondary dataset using, for example, a linear function, a logarithmic function, a hyperbolic inverse sine function, or a biexponential function. In some cases, the method includes calculating a dimensionality reduction of a plurality of secondary datasets including data points collected from a plurality of secondary samples.

[0012] Aspects of the present invention also include systems and non-transitory computer-readable storage media configured to perform the present methods (e.g., as described above and herein). For example, aspects of the present invention include a processor comprising a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to receive a secondary data set comprising data points collected from a secondary sample and calculate a second dimensionality reduction of the secondary data set based on a first dimensionality reduction of a reference data set comprising data points collected from a reference sample. The second dimensionality reduction calculated by the processor is compatible with the reduced reference data set. Similarly, aspects of the present invention include a non-transitory computer-readable storage medium comprising instructions stored thereon for performing dimensionality reduction by a method comprising receiving a secondary data set comprising data points collected from a secondary sample and calculating a second dimensionality reduction of the secondary data set based on a first dimensionality reduction of a reference data set comprising data points collected from a reference sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention may be better understood from the following detailed description when read in conjunction with the accompanying drawings, which include the following figures:

[0014] Figure 1 Depicted is the traditional dimensionality reduction workflow in flow cytometry.

[0015] Figure 2A dimensionality reduction workflow according to some embodiments of the present invention is depicted.

[0016] Figure 3 A conceptual illustration of a dimensionality reduction method according to an embodiment of the present invention is presented.

[0017] Figure 4 A dimensionality reduction workflow according to some embodiments of the present invention is presented.

[0018] Figure 5 Depicted is an exemplary graphical user interface for use during dimensionality reduction according to some embodiments of the present invention.

[0019] Figure 6 Depicted is a functional block diagram of a flow cytometer according to certain embodiments.

[0020] Figure 7 A control system according to some embodiments of the present invention is depicted.

[0021] Figures 8A to 8B Depicted is a schematic diagram of a particle sorting system according to certain embodiments.

[0022] Figure 9 An image-enabled particle sorter is depicted in accordance with certain embodiments.

[0023] Figure 10 A block diagram of a computing system is depicted in accordance with certain embodiments.

[0024] Figures 11A-11B Depicts the first sample ( Figure 11A ) and the second sample ( Figure 11B ) is an exemplary dimensionality reduction performed.

[0025] Figures 12A-12B Depicted is an exemplary dimensionality reduction using an overlay to display embeddings.

[0026] Figures 13A-13B Depicted is an exemplary dimensionality reduction using an overlay to display embeddings.

[0027] Figures 14A-14B It shows how to use input and output quality scores to gate events with high-quality embedding results. DETAILED DESCRIPTION

[0028] A computer-implemented dimensionality reduction method is provided. The method of interest includes receiving a secondary dataset comprising data points collected from a secondary sample; and calculating a second dimensionality reduction of the secondary dataset based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample. The calculated second dimensionality reduction is compatible with the reduced reference dataset. Also provided are systems and non-transitory computer-readable storage media configured to perform the method of the present invention.

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

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

[0031] Certain ranges presented herein are preceded by the term "about." The term "about" is used herein to provide literal support for the exact number preceding it, as well as a number that is close to or approximately the number preceding it. In determining whether a number is close to or approximately a specifically recited number, a number that is close to or approximately an unrecited number may be a number that, in the context in which it is presented, provides a substantial equivalence 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, only representative illustrative methods and materials are described herein.

[0033] All publications and patents cited in this specification are incorporated herein by reference to the same extent as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein to disclose and describe the methods and / or materials in connection with the cited publications. Citation of any publication is based on 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. Furthermore, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0034] It is important to note that, as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is also important to note that claims may be drafted to exclude any optional elements. Therefore, this statement is intended to serve as antecedent authority for the use of exclusive terminology such as "only" or "only" when reciting claim elements or for the use of negative limitations.

[0035] It will be apparent to those skilled in the art after reading this disclosure that each embodiment described and illustrated herein has independent components and features that can be easily separated or combined with the features of any of the other embodiments without departing from the scope or spirit of the invention. Any of the methods described can be performed in the order of events described or in any other logically feasible order.

[0036] While the systems and methods have been or will be described for purposes of grammatical fluency and functional explanation, it must be expressly understood that, unless expressly formulated pursuant to 35 U.S.C. § 112, the claims should not be construed as necessarily limited in any manner by construction of “means” or “step” limitations, but should be given the full scope of meaning and equivalents provided by the definitions in the claims under the doctrine of equivalents, and to the extent the claims are expressly formulated pursuant to 35 U.S.C. § 112, the full statutory equivalents set forth in 35 U.S.C. § 112 should be given.

[0037] Computer-implemented dimensionality reduction methods

[0038] As described above, aspects of the present invention include computer-implemented dimensionality reduction methods. The term "dimensionality reduction" in its conventional sense refers to converting data from a high-dimensional space into a relatively low-dimensional space. Typically and / or ideally, the data of dimensionality reduction (i.e., the data in the low-dimensional space) retains the properties of the data in the high-dimensional space, so that the properties are embodied in the data of dimensionality reduction in some form. The method of the present invention enhances the conversion of data from a high-dimensional space to a relatively low-dimensional space by, for example, improving data quality and providing a means for notifying the user when data quality is insufficient. In addition, the method according to some embodiments of the present invention can improve computational efficiency by 5% or more, such as 10% or more, such as 15% or more, such as 20% or more, such as 25% or more, and including 30% or more.

[0039] The method of the present invention involves calculating a second dimensionality reduction of a secondary data set based on a first dimensionality reduction of a reference data set (including data points collected from a reference sample). The second dimensionality reduction calculated by the present method is compatible with the reduced reference data set. By "compatible", it is meant that the second dimensionality reduction enables a meaningful comparison between the secondary data set and the reduced reference data set. As described in the background section above, previous dimensionality reduction methods are non-deterministic, that is, they will produce different results if run multiple times. In contrast, the compatibility of the second dimensionality reduction with the reduced reference data set means that the data clusters in the reduced data set generated by the two-dimensional dimensionality reduction calculated by the present method will have identifiable characteristics, that is, even if the second dimensionality reduction is run multiple times, they can be evaluated relative to the corresponding clusters in the reduced reference data set. For example, cells of the same or similar phenotype that are grouped together in clusters in the reduced data set generated by the second dimensionality reduction calculated by the present method can have the same or similar orientation relative to the corresponding clusters in the reduced reference data set. In addition, cells of the same or similar phenotype that are grouped together in clusters in the reduced dataset generated by the second dimensionality reduction calculated by the present method can have the same or similar number of clusters relative to corresponding clusters in the reduced reference dataset. In some cases, multiple iterations of the second dimensionality reduction of the present invention will result in the reduced data having a difference in cluster identification features (e.g., number of clusters, orientation, etc.) between iterations of 10% or less, such as 9% or less, such as 8% or less, such as 7% or less, such as 6% or less, such as 5% or less, such as 4% or less, such as 3% or less, such as 2% or less, and including 1% or less.

[0040] The dimensionality reduction of the present invention can take any suitable form. In some embodiments, the dimensionality reduction is performed by the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The t-SNE algorithm is described in "Learning Research" (2008) by Laurens van der Maaten and Geoffrey Hinton; it is incorporated herein by reference. In some embodiments, the dimensionality reduction is performed by the uniform manifold approximation and projection (UMAP) algorithm. The UMAP algorithm is described in "ARXIV" (2018) by McInnes et al.; it is incorporated herein by reference. In some embodiments, the dimensionality reduction is performed by the TriMap algorithm. The TriMap algorithm is described in "ARXIV" (2019) by Ehsan Amid and Manfred K. Warmuth; it is incorporated herein by reference. In some embodiments, the dimensionality reduction is performed by the EmbedSOM algorithm. The EmbedSOM algorithm is described in, for example, bioRxiv. (2018) by Kratochvíl et al., which is incorporated herein by reference. In some cases, the dimensionality reduction is performed by the PacMAP algorithm. The PacMAP algorithm is described in, for example, The Journal of Machine Learning Research. (2021) 22(1): 9129-9201 by Wang et al., the disclosure of which is incorporated herein by reference. In some cases, the first dimensionality reduction and the second dimensionality reduction are selected from tSNE, UMAP, EmbedSOM, TriMap, and PacMAP, although any currently existing or yet-to-be-invented dimensionality reduction may be applicable to this invention.

[0041] The methods of the present invention include receiving a secondary data set comprising data points collected from a secondary sample. The secondary data set can be received from any convenient source. In some embodiments, the secondary data set is received from a flow cytometer. In these embodiments, the secondary data set is considered flow cytometer data and is received from any suitable flow cytometer, including but not limited to those described herein. Exemplary flow cytometers include the BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6 Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TMFlow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSYmphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TM X-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TM Flow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter, BD Biosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, BD Biosciences FACSAria TM II cell sorter, BD Biosciences FACSAria TM III cell sorter, BD Biosciences FACSAria TM Fusion Cell Sorter and BD Biosciences FACSMelody TM Cell sorter, BD Biosciences FACSymphony TMS6 cell sorter, etc. In some cases, the secondary data set is received from a database. In this case, the database can be locally hosted, for example, hosted on any of a variety of known or future storage devices. Examples include any commonly available random access memory (RAM), magnetic media (such as a resident hard drive or tape), optical media (such as a read-write optical disc), flash memory device or other memory storage device. Alternatively, the database can be an Internet database. Exemplary Internet databases include, but are not limited to, the FlowRepository database (flowrepository(dot)org). In some cases, if the secondary data set includes flow cytometer data, the data points in the data set can be considered "events". In some embodiments, the secondary data set is provided as a .fcs file.

[0042] In some cases, the method includes receiving a plurality of secondary data sets. For example, in some cases, the method includes receiving a plurality of secondary data sets ranging in number from 2 to 50, such as 2 to 25 and including 2 to 10. In some cases, the method includes receiving 2 or more secondary data sets, such as 3 or more secondary data sets, such as 4 or more secondary data sets, such as 5 or more secondary data sets, such as 6 or more secondary data sets, such as 7 or more secondary data sets, such as 8 or more secondary data sets, such as 9 or more secondary data sets, and including 10 or more secondary data sets. In some such versions, the method includes calculating dimensionality reduction for a plurality of secondary data sets comprising data points collected from a plurality of secondary samples.

[0043] In addition, the method may include receiving a reference data set and performing a first dimensionality reduction. In such embodiments, the first dimensionality reduction may be performed according to any suitable dimensionality reduction method. For example, in some embodiments, the first dimensionality reduction is selected from tSNE, UMAP, EmbedSOM, TriMap, and PacMAP. Typically, the same type of dimensionality reduction is applied to the secondary data set and adjusted as described below. Alternatively, the reference data set and the first dimensionality reduction may be stored on a memory operably coupled to a processor configured to perform the present method. In some cases, the reference data set is a uniformly downsampled reference data set or a density-based downsampled reference data set. The reference data set may be received from any convenient source, including but not limited to the sources discussed above with respect to the secondary data set. In some cases, the method includes receiving multiple reference data sets. For example, in some cases, the method includes receiving multiple reference data sets ranging in number from 2 to 50, such as 2 to 25, and including 2 to 10.

[0044] In some embodiments, calculating the second dimensionality reduction includes determining data points collected from the reference sample that are closest to each data point collected from the secondary sample relative to a given data parameter. "Parameter" refers to the characteristic of the analyte being measured. For example, in an embodiment where the reference data set and the secondary data set include flow cytometer data, the parameter may include, for example, forward scatter (FSC), side scatter (SSC), CD3, CD4, CD8, CD25, etc. In some cases, the parameter is a fluorescence parameter, that is, the feature of interest is measured by its combination with a fluorescent molecule (e.g., a fluorochrome). The parameters discussed herein may also be embedding parameters, for example, parameters related to the type of dimensionality reduction being performed (e.g., t-SNE, UMAP, EmbedSOM, PacMAP, etc.). In selected cases, determining the data point closest to each data point collected from the reference sample and collected from the secondary sample is performed for parameters known to exist in both the reference data set and the secondary data set. In some cases, the secondary data set includes data points associated with multiple parameters that match the reference data set. In other words, these parameters are common to both data sets. For example, if the reference dataset for dimensionality reduction has a parameter set of {CD3, CD4, CD8, CD25, tSNE1, tSNE2}, and the secondary dataset has a parameter set of {CD3, CD4, CD8, and CD25}, then the calculations can be performed using these four common parameters {CD3, CD4, CD8, and CD25}.

[0045] In some cases, calculating the second dimensionality reduction includes a neighbor search configured to find one or more points in the set that are closest to one or more data points collected from the secondary sample. For example, in some cases, the method includes calculating the nearest neighbors of each data point collected from the secondary sample. The nearest neighbor problem can be defined as finding the point closest to q in S, where S is a set of points in space M, q∈M. In selected cases, the method includes calculating the k nearest neighbors in the reference data set for each data point collected from the secondary sample, where k is a positive integer. Although the value of k can vary, in some embodiments, k ranges from 1 to m, where m is equal to half the number of data points in the reference sample. In some cases, k ranges from 2 to 5. In some cases, k is 1 or greater, such as 2 or greater, such as 3 or greater, such as 4 or greater, such as 5 or greater, such as 6 or greater, such as 7 or greater, such as 8 or greater, such as 9 or greater, and including 10 or greater. The method of calculating the k nearest neighbors can vary. In some cases, the method includes using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, a locality-sensitive hashing, a hierarchical navigable small world, an approximate nearest neighbor with a random projection tree, a GPU-based KNN search, or a brute-force KNN search to calculate the k nearest neighbors. In some cases, the method includes using a vantage point tree to calculate the k nearest neighbors. Advantage point trees are described in documents such as Yianilos, Peter N. Soda. (1993) 93 (194): 311-21, which are incorporated herein by reference. In selected cases, the method includes using a k-dimensional tree (kd tree) to calculate the k nearest neighbors. K-dimensional trees are described in, for example, Bentley, JL Comm. ACM. (1975) 18 (9): 509-517, which are incorporated herein by reference. In selected cases, the method includes using a ball tree (metric tree) to calculate the k nearest neighbors. Ball trees are described in, for example, Omohundro, SM's five ball tree construction algorithms (1989), which are incorporated herein by reference. In some cases, the method includes computing the k nearest neighbors using locality sensitive hashing. Locality sensitive hashing is described, for example, in Paulevé et al., Pattern Recognition Letters (2010) 31(11):1348-1358, incorporated herein by reference. In some cases, the method includes computing the k nearest neighbors using hierarchical navigable small worlds (HNSW). Hierarchical navigable small worlds is described, for example, in Malkov et al., IEEE Transactions on Pattern Analysis and Machine Intelligence (2018) 42(4):824-836, incorporated herein by reference. In some cases, the method includes computing the k nearest neighbors using approximate nearest neighbors with random projection trees. Such trees are described, for example, in et al., in the 2016 IEEE International Conference on Big Data (Big Data), pp. 881-888, incorporated herein by reference. In some cases, the method includes computing k nearest neighbors using a GPU-based KNN search. GPU-based KNN search is described, for example, in Garcia et al., in the 2010 IEEE International Conference on Image Processing, pp. 3757-3760, incorporated herein by reference. In some cases, the method includes performing a brute-force KNN search.

[0046] The method according to some embodiments further includes calculating the distance between each data point collected from the secondary sample and each of the k nearest neighbors. The calculated distances may vary. In some cases, the distance is the Euclidean distance, which is the length of the line segment between the two points. In other cases, the distance is the Manhattan distance, where the distance between two points is the sum of the absolute differences of their Cartesian coordinates. In other cases, the distance is the Chebyshev distance, which is the maximum difference between two vectors along any coordinate dimension. In other cases, the distance is the Minkowski distance, which is a generalization of the Euclidean distance and the Manhattan distance. In other cases, the distance is the cosine distance, which is the complement of the cosine similarity. In some cases, the distance is selected from the group consisting of the Euclidean distance, the Manhattan distance, the Chebyshev distance, the Minkowski distance, and the cosine distance.

[0047] In an embodiment, the method includes calculating the second dimensionality reduction based on the weighted average of the positions of the k nearest neighbors. In other words, the method includes taking a weighted average of the derived parameters in the reference data set. In other words, the position of each data point in the secondary data set in the space of dimensionality reduction is defined by the weighted position of its nearest neighbor from the high-dimensional space in the space of dimensionality reduction. The weighted average can be calculated using the weight of each nearest neighbor in the k nearest neighbors obtained based on distance (e.g., Euclidean distance, Manhattan distance, Chebyshev distance, Minkowski distance and cosine distance as needed). In other words, the nearest neighbors are weighted differently in the average value according to their distance. The closer the neighbor point, the greater the weight. The weight used for the weighted average can be different. In some cases, the method includes obtaining weights as follows:

[0048]

[0049] where w i is the weight, d i is a distance, and a is a real number. In other cases, the method includes obtaining the weight as follows:

[0050]

[0051] Where f is any monotone completely non-decreasing function, w iis the weight, d i is the distance, a and b are real numbers. Otherwise, the method includes obtaining the weights as follows:

[0052]

[0053] where w i is the weight, d i is the distance. As shown in the above formula, the weight increases as the distance decreases.

[0054] In some embodiments, the method includes creating derived parameters for the secondary data set based on the parameters of the reference data set of dimensionality reduction. In this case, these parameters are selected from fluorescence parameters, scattering parameters, imaging parameters or classification parameters. In certain embodiments, the derived parameters are fluorescence parameters. For example, the computer-implemented method of the present invention can be used to create a "virtual tube". In other words, assuming that there is a backbone of shared parameters between the reference data set / reference sample and the secondary data set / secondary sample, the values of the fluorescence parameters that do not exist for the secondary data set can be estimated based on those parameters in the reference data set. This will allow users to create "virtual tubes" to visualize the co-expression patterns of markers that are not present together in any single tube (i.e., data set). In other embodiments, the derived parameters are scattering parameters. In further embodiments, the derived parameters are imaging parameters. When the parameters are fluorescence parameters, scattering parameters or imaging parameters, the parameters can be calculated by the above-mentioned weighted average. In some embodiments, the parameters are classification parameters, such as clustering parameters. In such embodiments, the calculation is performed by weighted majority voting. For example, if a data point's seven nearest neighbors belong to clusters {1, 1, 1, 1, 2, 1, 3}, then the data point would be assigned to cluster 1 because the majority of neighbors belong to that cluster. For data points whose nearest neighbors lack a majority cluster, the data point can be assigned to cluster -1, i.e., an outlier cluster. Alternatively, the method can include placing all data points in one cluster, for example, if the experiment requires that all cells be classified. In this case, the method includes assigning the data point to the same cluster as the single nearest neighbor.

[0055] In some cases, creating derived parameters for the secondary dataset involves interpolation. In other words, the parameters for the data points in the secondary dataset are interpolated based on known data points in the reference dataset and the reduced reference dataset. In some cases, the interpolation is nearest neighbor interpolation. In other cases, the interpolation is linear interpolation. This process can be used for continuous variables, such as dimensionality reduction parameters, for example.

[0056] According to some embodiments, the method includes calculating a quality score designed to indicate whether the data is sufficiently stable over time. In some cases, a user can review one or more quality scores to determine the quality of the secondary dimensionality reduction. For example, the quality score can indicate the extent to which the reference dataset and the secondary dataset can be meaningfully compared. In some cases, the quality score can indicate the accuracy of the parameters derived for the secondary dataset. In some embodiments, the method includes calculating an input quality score, which indicates how closely each data point collected from the secondary sample correlates with the data points in the reference dataset. In some cases, the input quality for a given cell is the average of the distances of its nearest neighbors to that cell, normalized by the number of dimensions used. In short, the smaller the number of dimensions, the more representative the nearest neighbors are of the target cell. The normalization method can vary depending on the distance metric used. In embodiments where the distance is Manhattan distance, normalization is performed by dividing by the number of dimensions used in the calculation. In embodiments where the distance is Euclidean distance, normalization can be performed by dividing by the square root of the number of dimensions used in the calculation. In embodiments where the distance is Chebyshev distance, normalization is not required because the scores are already normalized. If normalization is used, it may result in a score that is reasonably independent of the dimensionality and distance metric used.

[0057] In other embodiments, the method includes calculating an output quality score that measures the separation of the k nearest neighbors in the first reduced dataset. In some cases, calculating the output quality score includes obtaining the normalized average distance of each of the k nearest neighbors in the reduced reference dataset. In other words, the output quality for a given cell is the average normalized distance between the nearest neighbor cells in the embedding parameter space. The smaller the value, the better. A larger value may indicate that the nearest neighbors in the measured parameter space have been significantly separated in the reduced space (e.g., tSNE partitions a homogeneous population into separate islands). Normalization can be performed as described above for the input quality score.

[0058] The method may also include transforming the reference data set and the secondary data set. Specifically, the method according to some embodiments includes transforming the parameters in the reference data set and the parameters in the secondary data set. In some embodiments, the method includes using a linear function, a logarithmic function, a hyperbolic inverse sine function, or a biexponential function to transform the parameters in the reference data set and the parameters in the secondary data set. In some embodiments, the method includes using a linear function to transform the parameters in the reference data set and the parameters in the secondary data set. In some embodiments, the method includes using a logarithmic function to transform the parameters in the reference data set and the parameters in the secondary data set. In some embodiments, the method includes using a hyperbolic inverse sine function to transform the parameters in the reference data set and the parameters in the secondary data set. In some such embodiments, the hyperbolic inverse sine function is performed using a cofactor of 150. In some embodiments, the method includes using a biexponential function to transform the parameters in the reference data set and the parameters in the secondary data set.

[0059] Figure 2 A dimensionality reduction method according to an embodiment of the present invention is presented. A first dimensionality reduction is performed on a reference dataset 201a comprising data points collected from a reference sample. This generates a reduced dimensionality reference dataset comprising derived parameters 202a. The reference dataset 201a and derived parameters 202a are used to calculate a second dimensionality reduction of a secondary dataset 201b, which generates parameters 202b that are compatible with the derived parameters 202a.

[0060] Figure 3 A conceptual diagram of a dimensionality reduction method according to an embodiment of the present invention is presented. First, a reference dataset from a reference sample is subjected to a dimensionality reduction R from a dimension D. Subsequently, the method includes computing a dimensionality reduction R of a secondary dataset that is compatible with the reduced reference dataset. This involves, for each data point (event e) in the secondary dataset: computing NN as the k nearest neighbors of e in the reference dataset using dimension D. Figure 3 In the example, the nearest neighbors nn1, nn2 and nn3 are depicted. The method also includes, for each nearest neighbor nn of NN i , calculate the distance d i As from nn i The distance to e. Figure 3 In the example, the distance d1 between nn1 and e is 1, the distance d2 between nn2 and e is 2, and the distance d3 between nn3 and e is 4.

[0061] Next, the method includes calculating the input quality score of e as d iThe average of the values of , normalized by the number of dimensions in D. In other words, the input quality is the normalized average distance of e to its nearest neighbor in D, where closer neighbors are better, i.e., smaller numbers are better. In this example, the input quality score is calculated as ((1+2+4) / 3) / ND, where ND varies depending on the type of distance metric used. If the distance is Manhattan distance, ND=||D||. If the distance is Euclidean distance, ND=sqrt(||D||). If the distance is Chebyshev distance, ND=1. Subsequently, the method includes calculating the output quality score of e as nn in R i and nn j The output quality score is the average normalized distance between the nearest neighbors in R, where i!=j. In other words, the output quality is the normalized average distance between the nearest neighbors in R, with closer neighbors being better, meaning lower numbers are better. In this example, the distances are calculated as follows: |nn1–nn2|=5.5, |nn2–nn3|=4, and |nn1–nn3|=5.7. The output quality score is calculated as ((5.5+4+5.7) / 3) / NR, where NR varies depending on the distance metric used. When the distance is Manhattan distance, NR=||R||. When the distance is Euclidean distance, NR=sqrt(||R||). When the distance is Chebyshev distance, NR=1.

[0062] The data points identified as nearest neighbors in the reference dataset are then used to create derived parameters for the data points in the secondary dataset, for example using linear interpolation for continuous variables (such as dimensionality reduction parameters). This involves taking a weighted average of the derived parameters in the reference dataset. The weighting is based on how close a given cell is to each nearest neighbor. In other words, for each d i , calculate the weight w i w i =1 / (1+d i ).exist Figure 3 In the example, the weight w1 for nn1 is 1 / 2, the weight w2 for nn2 is 1 / 3, and the weight w3 for nn3 is 1 / 5. i For each reduced dimension r of R as a weight, the method consists in computing the r-value of e as the weighted average of the r-values of the nearest neighbor NNs.

[0063] Figure 4 A dimensionality reduction workflow involving deriving fluorescence parameters according to certain embodiments of the present invention is presented. Figure 4As shown, reference dataset 401a and secondary dataset 401b share a common backbone of parameters / markers D. Reference dataset 401a has parameters / markers R1 that are specific to the dataset of reference sample S1, while secondary dataset 401b has parameters / markers R2 that are specific to the dataset of secondary sample S2. The method of the present invention (e.g., as described above) can be used to create a "virtual tube" 402 for the secondary sample, wherein the tube-specific markers R1 are estimated in the secondary dataset.

[0064] In some cases, the method includes receiving input from a user. This input may relate, for example, to which parameters and / or quality scores should be derived for the secondary dataset and / or calculated. In some cases, the user selects parameters known to be common to both datasets, i.e., parameters in the secondary dataset that match the reference dataset. In some cases, the method includes entering a value for k for the k-nearest neighbor search described above. In selected embodiments, the method includes selecting a distance metric to be used for the calculation (e.g., Manhattan distance, Euclidean distance, Chebyshev distance, Minkowski distance, or cosine distance). In some embodiments, the method includes selecting the number of data points in the reference dataset to be used for the calculation. In other cases, the user may select whether to calculate an input quality score. In still other cases, the user may select whether to calculate an output quality score. These selections may be implemented, for example, by an input manager. In some cases, the input manager is operably coupled to a graphical user interface through which input is entered. In some cases, input is entered through an internet website menu interface (e.g., at a remote location) and transmitted to the input manager via the internet or a local area network. In some embodiments, the input manager is operably coupled to one or more searchable parameter databases (e.g., directories). In some cases, the input manager includes a database of parameters. All or part of each database parameter can be displayed on a graphical user interface, such as in the form of a list, drop-down menu, or other configuration (e.g., tiles). For example, the graphical user interface can display a list of parameters simultaneously (i.e., on a single screen), or can contain one or more drop-down menus. In some cases, the graphical user interface includes multiple drop-down menus: a first menu for selecting parameters in the reference dataset and a second menu for selecting target parameters in the secondary dataset to be exported and embedded. In other embodiments, information can be entered into appropriate text fields, check boxes can be selected, one or more items can be selected from a drop-down menu, or by using a combination thereof. The methods of the present invention can include using parameters selected for the methods of the present invention.

[0065] Figure 5An exemplary graphical user interface 500 for receiving input from a user is depicted. The graphical user interface 500 includes a menu 502 for selecting parameters that are common between the reference dataset and the secondary dataset, and a menu 503 for selecting parameters to be exported / embedded in the secondary dataset during the dimensionality reduction process. Figure 5 In the example of FIG, check boxes 503 and 504 are provided for indicating whether an input quality score and an output quality score should be calculated, respectively. The graphical user interface 500 also includes a text field 505 for indicating the maximum size of the reference data set. The maximum training size limits the search area used to detect the nearest neighbors, essentially downsampling the input before finding the nearest neighbor. The user can also indicate the number of nearest neighbors (i.e., k) used to calculate the k nearest neighbors within the reference data set for each data point collected from the secondary sample in text field 506. The graphical user interface 500 also includes a drop-down menu 507 for selecting a distance metric.

[0066] As described above, in some embodiments, the reference data set and the secondary data set may include flow cytometer data. In some cases, the flow cytometer data is fluorescence flow cytometer data. "Fluorescence flow cytometer data" refers to parameter information about a sample (e.g., cell, particle) in a flow cell collected by any number of fluorescence detectors in a particle analyzer. In an embodiment, the fluorescence flow cytometer data includes signals from a plurality of different fluorochromes such as, for example, ranging from 2 to 40 different fluorochromes, including 3 to 30 different fluorochromes, such as 3 to 20 different fluorochromes, and in some cases including 3 to 5 different fluorochromes. In some embodiments, the plurality of different fluorochromes includes 2 or more different fluorochromes, including 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 20 or more, 25 or more, and 30 or more different fluorochromes. Fluorescence flow cytometer data can be acquired by any convenient protocol, including the protocols described below.

[0067] In some embodiments, the method includes generating one or more population clusters based on determined parameters of analytes (e.g., cells, particles) in the sample. As used herein, a "population" or "subpopulation" of an analyte, such as cells or other particles, generally refers to a group of analytes that have properties (e.g., optical, impedance, or time properties) about one or more measured fluorescence parameters such that the measured parameter data form clusters in the data space. Thus, populations are identified as clusters in the data. In contrast, each data cluster is typically interpreted as a population corresponding to a particular type of cell or analyte, although clusters corresponding to noise or background are also typically observed. Clusters can be defined in subsets of dimensions, for example, a subset of measured fluorescence parameters (i.e., fluorochromes) corresponding to populations that differ only in a subset of the measured parameters or features extracted from the measurement of the sample.

[0068] The generation method of the flow cytometer data used in the present invention can be changed as needed.For example, the sample containing particles can be irradiated with the light from the light source (for example, in the stream of the flow cytometer). In certain embodiments, the light source is a broadband light source, emitting light with a wide wavelength range, for example, across 50nm or longer, such as 100nm or longer, such as 150nm or longer, such as 200nm or longer, such as 250nm or longer, such as 300nm or longer, such as 350nm or longer, such as 400nm or longer, and including spanning 500nm or longer. For example, a suitable broadband light source emission wavelength is the light of 200nm to 1500nm. Another example of a suitable broadband light source includes a light source that emits a wavelength of 400nm to 1000nm. When the method includes irradiating with a broadband light source, the broadband light source protocol of interest can include but is not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stable fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated white light source and other broadband light sources or its arbitrary combination.

[0069] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or a narrow wavelength range, for example, using a light source that emits light in a narrow wavelength range, for example, a range of 50 nm or less, such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less, and including a light source that emits light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, the narrowband light source protocol of interest can include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0070] Aspects of the present invention include using fluorescence detectors to collect fluorescence. In some cases, fluorescence detectors can be configured to detect the fluorescence emission from fluorescent molecules (for example, the specific binding members of the label associated with the particle in the flow cell (such as, the labeled antibody specifically bound to the marker of interest)). In certain embodiments, method includes using one or more fluorescence detectors to detect the fluorescence from sample, such as 2 or more, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, and including 25 or more fluorescence detectors. In an embodiment, each fluorescence detector is configured to generate fluorescence data signal. Fluorescence from sample can be independently detected by each fluorescence detector in one or more wavelength ranges of 200nm to 1200nm. In some cases, the method includes detecting fluorescence from the sample within a certain wavelength range, such as 200nm to 1200nm, such as 300nm to 1100nm, such as 400nm to 1000nm, such as 500nm to 900nm, and including from 600nm to 800nm. In other cases, the method includes using each fluorescence detector to detect fluorescence at one or more specific wavelengths. For example, fluorescence can be detected at one or more of the following wavelengths: 450nm, 518nm, 519nm, 561nm, 578nm, 605nm, 607nm, 625nm, 650nm, 660nm, 667nm, 670nm, 668nm, 695nm, 710nm, 723nm, 780nm, 785nm, 647nm, 617nm and any combination thereof, depending on the number of different fluorescence detectors in the target light detection system. In certain embodiments, the method includes detecting the wavelength of light corresponding to the fluorescence peak wavelength of certain fluorochromes present in the sample. In an embodiment, fluorescence flow cytometry data is received from one or more fluorescence detectors (e.g., one or more detection channels), such as 2 or more, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, and including 8 or more fluorescence detectors (e.g., 8 or more detection channels).

[0071] In some embodiments, the data sets described herein can be further analyzed and / or processed. Additional processing methods that can be used in conjunction with the methods of the present disclosure are described, for example, in U.S. Patent Nos. 11,506,593 and 11,674,879; and U.S. Patent Application Publication Nos. 2021 / 0358566 and 2022 / 0390349, the disclosures of which are incorporated herein by reference in their entirety.

[0072] Reference sample and secondary sample can be different.In an embodiment, reference sample and secondary sample are taken from same organism or same organism type (for example, species).In certain embodiments, reference sample and secondary sample are produced or prepared in the same manner or using the same scheme.In some cases, the sample analyzed in this method is a biological sample.Term " biological sample " is used with its conventional meaning, refers to the subset of whole organism, plant, fungus or animal tissue, cell or component part (it can be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic umbilical cord blood, urine, vaginal secretions and semen in some cases).Therefore, " biological sample " refers to the subset of protist organism or its tissue, also refers to the homogenate, lysate or extract prepared by the subset of this organism or its tissue, including but not limited to such as blood plasma, serum, spinal fluid, lymph, skin slice, respiratory tract, gastrointestinal tract, cardiovascular and urogenital tract slice, tear, saliva, milk, blood cell, tumor and organ. A biological sample can be any type of biological tissue, including both healthy tissue and diseased tissue (e.g., cancerous tissue, malignant tissue, necrotic tissue, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, such as plasma, tears, urine, semen, etc. In certain cases, the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or finger stick (the blood may or may not be mixed with any reagents, such as preservatives, anticoagulants, etc., prior to the assay).

[0073] In certain embodiments, the source of the sample is "mammal" or "mammals," wherein these terms are widely used to describe organisms within the class Mammals, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method can be applied to samples obtained from human subjects of both sexes and at any stage of development (i.e., newborns, infants, infancy, adolescence, adulthood), wherein in certain embodiments, the human subject is infancy, adolescence, or adulthood. Although the present invention can be applied to samples from human subjects, it should be understood that the method can also be implemented with samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0074] The cells of interest can be targeted and characterized according to a variety of parameters (such as phenotypic characteristics identified by attaching specific fluorescent markers to the cells of interest). In some embodiments, the system is configured to deflect analyzed droplets that have been determined to include target cells. This method can be used to characterize a variety of cells. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells with suitable cell surface markers or antigens, which can be captured or labeled by suitable affinity agents or their conjugates. For example, the target cell may include cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminal), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, the target cell is selected from cells containing HIV, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) from whole blood, bone marrow, or umbilical cord blood.

[0075] The method of interest can also include the use of particles in research, laboratory testing or treatment. In some embodiments, the method includes preparing individual cells from a target fluid or tissue biological sample. For example, the method includes obtaining cells from a fluid or tissue sample to serve as a research or diagnostic sample for diseases such as cancer. Similarly, the method includes obtaining cells from a fluid or tissue sample for treatment. A cell therapy protocol is a protocol in which living cell material (including, for example, cells and tissues) is prepared and introduced into a subject's body as a treatment. Diseases that can be treated by administering samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system diseases, organ damage, and the like.

[0076] A typical cell therapy protocol may include the following steps: sample collection, cell separation, genetic modification, in vitro culture and amplification, cell harvesting, sample volume reduction and cleaning, biological preservation, storage, and introduction of cells into the subject. The protocol may first collect living cells and tissues from the subject's source tissue to prepare a sample of cells and / or tissues. The sample may be collected by any suitable procedure, such as administering a cell mobilization agent to the subject, drawing blood from the subject, removing bone marrow from the subject, etc. After collecting the sample, cell enrichment may be performed by a variety of methods, including, for example, centrifugation-based methods, filtration-based methods, elutriation, magnetic separation, fluorescence-activated cell sorting (FACS), etc. In some cases, the enriched cells may be genetically modified by any convenient method, such as nuclease-mediated gene editing. Genetically modified cells may be cultured, activated, and amplified in vitro. In some cases, the cells are preserved (e.g., cryopreserved) and stored for future use, wherein the cells are thawed and then administered to the patient, for example, the cells may be injected into the patient.

[0077] system

[0078] Aspects of the present invention further include systems. A system of interest includes a processor comprising a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform a method of the present invention (e.g., as described above). Specifically, the processor is configured to receive a secondary dataset comprising data points collected from a secondary sample and calculate a second reduced dimension of the secondary dataset based on a first reduced dimension of a reference dataset comprising data points collected from a reference sample. As described above, the second reduced dimension is compatible with the reduced reference dataset.

[0079] In some cases, the present processor operates in conjunction with programmable logic, which can be implemented in hardware, software, firmware, or any combination thereof, to, for example, compute a second dimensionality reduction of a secondary data set based on a first dimensionality reduction of a reference data set comprising data points collected from a reference sample. For example, when the programmable logic is implemented in software, the method can be implemented at least in part by a computer-readable data storage medium comprising program code, the program code comprising instructions that, when executed, are configured to compute the second dimensionality reduction of the secondary data set based on the first dimensionality reduction of the reference data set comprising data points collected from the reference sample. For example, the program code can include instructions for determining data points collected from the reference sample that are closest to each data point collected from the secondary sample with respect to a given data parameter, for example, by computing k nearest neighbors within the reference data set for each data point collected from the secondary sample. The processor can also be configured to perform any of the other method steps described above.

[0080] The programmable logic can be implemented in any of a variety of devices, such as a specially programmed event processing computer, a wireless communication device, an integrated circuit device, and the like. In some embodiments, the programmable logic can be executed by a specially programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. A combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration that is at least partially data connected, can implement one or more features described herein. In some cases, the system further includes a display configured to output the results of the method (e.g., a dimensionality reduction plot of the data, etc.). The display may include, but is not limited to, a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0081] In an embodiment, the system is configured to be used in software or analysis tools for analyzing flow cytometry data, such as FlowJo TM (Ashland, OR)). Data were analyzed using FlowJo. TM FlowJo is a software package developed by FlowJo LLC (a subsidiary of Becton Dickinson) for analyzing flow cytometric data. The software is configured to manage flow cytometric data and generate graphical reports on them (https: / / www(dot)flowjo(dot)com / learn / flowjo-university / flowjo). The initial data can be analyzed in a data analysis software or tool such as FlowJo TM ) by appropriate methods, such as manual gating, cluster analysis or other computational techniques. The system or part thereof can be used as a software for analyzing data (such as FlowJo TM In these embodiments, the computer-controlled system according to the present disclosure can be implemented as a software component of an existing software package (such as FlowJo TM ) software "plug-in" to run.

[0082] In some embodiments, the present processor is used as part of or in conjunction with a flow cytometer. The flow cytometer of interest includes a flow cell. The term "flow cell" is described in its conventional meaning to refer to a component, such as a cuvette, including a flow channel having a liquid flow for transporting particles in a sheath fluid. The cuvette of interest includes a container having a channel running through it. The flow stream can include a liquid sample injected from a sample tube. The flow cell of interest includes a flow channel accessible to light. In some cases, the flow cell includes a transparent material (e.g., quartz) that allows light to pass through. In some embodiments, the flow cell is an air flow cell in which particles are optically detected in free space. In some cases, the flow stream is configured for illumination with light from one or more light sources at a detection point. As described herein, a "detection point" refers to an area within the flow stream where particles are illuminated by light from a light source, for example for analysis. The size of the detection point can vary as needed. For example, when 0 μm represents the optical axis emitted by the light-dependent light source, the detection zone can range from -100 μm to 100 μm, such as -50 μm to 50 μm, such as -25 μm to 40 μm, and including -15 μm to 30 μm. The flow stream for which the flow channel is configured can include a liquid sample injected from a sample tube. In certain embodiments, the flow stream can include a narrow, fast-flowing liquid stream arranged to separate linearly separated particles transported therein from each other in a single file. After the particles are illuminated in the flow cell, light modulated by the particles can be observed.

[0083] Any convenient flow cell for propagating the fluid sample to the sample detection area may be employed, wherein in some embodiments the flow cell comprises a cylindrical flow cell, a frustoconical flow cell, or a flow cell comprising a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having an orifice transverse to the longitudinal axis.

[0084] Aspects of the present system also include a light source for illuminating the flow stream at the detection point. Any convenient light source can be used, such as a laser. In some embodiments, the laser can be any convenient laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some cases, 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 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 present flow cytometer includes a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In 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 selenide (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 cases, the flow cytometer includes a solid-state laser, such as a ruby laser, an Nd:YAG laser, an NdCrYAG laser, an Er:YAG laser, an Nd:YLF laser, an Nd:YVO4 laser, an Nd:YCa4O(BO3)3 laser, an Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, an ytterbium YAG laser, an ytterbium2O3 laser, or a cerium-doped laser, and combinations thereof.

[0085] According to certain embodiments, the laser light source may further include one or more optical adjustment components. In certain embodiments, the optical adjustment component is located between the light source and the flow cell and may include any device capable of changing the spatial width of the irradiation or certain other characteristics of the light source irradiation (e.g., irradiation direction, wavelength, beam width, beam intensity, and focal spot). The optical adjustment scheme may include any convenient device for adjusting one or more characteristics of the light source, including but not limited to lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimation schemes, and combinations thereof. In certain embodiments, the flow cytometer of interest includes one or more focusing lenses. In one example, the focusing lens can be a reducing lens. In other embodiments, the flow cytometer of interest includes an optical fiber.

[0086] When the optical adjustment component is configured to move, the optical adjustment component can be configured to move continuously or in discrete intervals, such as in increments of 0.01 μm or greater, such as 0.05 μm or greater, such as 0.1 μm or greater, such as 0.5 μm or greater, such as 1 μm or greater, such as 10 μm or greater, such as 100 μm or greater, such as 500 μm or greater, such as 1 mm or greater, such as 5 mm or greater, such as 10 mm or greater, and including increments of 25 mm or greater.

[0087] Any displacement scheme can be used to move the optical adjustment component structure, such as coupling to a movable support table or directly to a motor-actuated translation stage, a screw translation assembly, a gear translation device, such as a device using a stepper motor, a servo motor, a brushless motor, a brushed DC motor, a micro-step drive motor, a high-resolution stepper motor, and other types of motors.

[0088] Light source can keep any suitable distance with flow cell, such as light source and flow cell are separated 0.005mm or more, such as 0.01mm or more, such as 0.05mm or more, such as 0.1mm or more, such as 0.5mm or more, such as 1mm or more, such as 5mm or more, such as 10mm or more, such as 25mm or more, and comprise 100mm or more distance.In addition, light source can be positioned at any suitable angle with respect to flow cell, such as the angle of scope from 10 degree to 90 degree, such as the angle from 15 degree to 85 degree, such as the angle from 20 degree to 80 degree, such as the angle from 25 degree to 75 degree, and comprise the angle from 30 degree to 60 degree, for example, at 90 degree angle.

[0089] According to certain embodiments, the system includes a plurality of light sources. In some cases, the plurality of light sources includes a plurality of lasers, such as 2 lasers or more, such as 3 lasers or more, such as 4 lasers or more, such as 5 lasers or more, such as 10 lasers or more, and including 15 lasers or more, which are configured to provide laser light for discretely irradiating the flow stream. Depending on the desired wavelength of light for irradiating the flow stream, each laser can have a specific wavelength ranging from 200nm to 1500nm, such as from 250nm to 1250nm, such as from 300nm to 1000nm, such as from 350nm to 900nm, and including from 400nm to 800nm. In certain embodiments, the laser of interest can include one or more of a 405nm laser, a 488nm laser, a 561nm laser, and a 635nm laser.

[0090] The system of interest may include one or more detectors for detecting particle-modulated light intensity data. In some embodiments, the particle-modulated light detector includes one or more forward-scattered light detectors configured to detect forward-scattered light. For example, the particle analyzer may include one forward-scattered light detector or multiple forward-scattered light detectors, such as two or more, such as three or more, such as four or more, and including five or more. In some embodiments, the particle analyzer includes one forward-scattered light detector. In other embodiments, the particle analyzer includes two forward-scattered light detectors.

[0091] Any convenient detector for detecting collected light can be used for the forward scattered light detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge coupled devices (CCDs), intensified charge coupled devices (ICCDs), light emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, and other detectors. In certain embodiments, the collected light is measured using a charge coupled device (CCD), a semiconductor charge coupled device (CCD), an active pixel sensor (APS), a complementary metal oxide semiconductor (CMOS) image sensor, or an N-type metal oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector is a photomultiplier tube, such as a photomultiplier tube having an effective detection surface area per region ranging from 0.01 cm 2 Up to 10cm 2 , such as from 0.05cm 2 Up to 9cm 2 , such as from 0.1cm 2 Up to 8cm 2 , such as from 0.5cm 2 Up to 7cm 2 As well as including from 1cm 2 Up to 5cm 2 Photomultiplier tube.

[0092] In embodiments, the forward scattered light detector is configured to measure light continuously or at discrete intervals. In some cases, the detector of interest is configured to measure the collected light continuously. In other cases, the detector of interest is configured to measure light at discrete intervals, such as every 0.001 millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, including every 1000 milliseconds or some other interval.

[0093] In some cases, the system includes one or more side scattered light detectors for detecting side scattered light wavelengths (i.e., light refracted and reflected from the surface and internal structure of the particles). In some embodiments, the particle analyzer includes a single side scattered light detector. In other embodiments, the particle analyzer includes multiple side scattered light detectors, such as 2 or more, such as 3 or more, such as 4 or more, and including 5 or more.

[0094] Any convenient detector for detecting collected light can be used for the side scattered light detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge coupled devices (CCDs), intensified charge coupled devices (ICCDs), light emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes and combinations thereof, and other detectors. In certain embodiments, 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 is used to measure the collected light. In certain embodiments, the detector is a photomultiplier tube, such as a photomultiplier tube having an effective detection surface area of 0.01 cm2 per region. 2 Up to 10cm 2 Photomultiplier tubes, such as those from 0.05 cm 2 Up to 9cm 2 , such as from 0.1cm 2 Up to 8cm 2 , such as from 0.5cm 2 Up to 7cm 2 As well as including from 1cm 2 Up to 5cm 2 .

[0095] In an embodiment, the system further comprises a fluorescence detector configured to detect one or more fluorescence wavelengths. In other embodiments, the particle analyzer comprises a plurality of fluorescence detectors, such as 2 or more, such as 3 or more, such as 4 or more, 5 or more, 10 or more, 15 or more, and including 20 or more.

[0096] Any convenient detector for detecting collected light can be used for the fluorescence detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge coupled devices (CCDs), intensified charge coupled devices (ICCDs), light emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, and other detectors. In certain embodiments, 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 is used to measure the collected light. In certain embodiments, the detector is a photomultiplier tube, for example, with an effective detection surface area of each region ranging from 0.01 cm 2 Up to 10cm 2 Photomultiplier tubes, such as those from 0.05 cm 2 Up to 9cm 2 , such as from 0.1cm 2 Up to 8cm 2 , such as from 0.5cm 2 Up to 7cm 2 , and including from 1cm 2 Up to 5cm 2 .

[0097] When the present particle analyzer includes multiple fluorescence detectors, each fluorescence detector can be identical, or the collection of fluorescence detectors can be a combination of different types of detectors. For example, when the present particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD type device, and the second fluorescence detector (or image sensor) is a CMOS type device. In other embodiments, the first fluorescence detector and the second fluorescence detector are both CCD type devices. In still other embodiments, the first fluorescence detector and the second fluorescence detector are both CMOS type devices. In still other embodiments, the first fluorescence detector is a CCD type device, and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS type device, and the second fluorescence detector is a photomultiplier tube. In still other embodiments, the first fluorescence detector and the second fluorescence detector are both photomultiplier tubes.

[0098] In embodiments of the present disclosure, the fluorescence detector of interest is configured to measure collected light of one or more wavelengths, such as 2 or more wavelengths, such as 5 or more different wavelengths, such as 10 or more different wavelengths, such as 25 or more different wavelengths, such as 50 or more different wavelengths, such as 100 or more different wavelengths, such as 200 or more different wavelengths, such as 300 or more different wavelengths, and including measuring 400 or more different wavelengths of light emitted by a sample in a flow stream. In some embodiments, two or more detectors incorporated into a particle analyzer described herein are configured to measure collected light of the same or overlapping wavelengths.

[0099] In some embodiments, the fluorescence detector of interest is configured to measure light collected within a certain wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the detector of interest is configured to collect a spectrum of light within a certain wavelength range. For example, a particle analyzer may include one or more detectors configured to collect a spectrum of light within one or more of the wavelength ranges from 200 nm to 1000 nm. In other embodiments, the detector of interest is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a particle analyzer 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, one or more detectors may be configured to pair with a specific fluorophore, such as a fluorophore used with a sample in a fluorescence analysis.

[0100] In some embodiments, one or more particle modulated light detectors include one or more detector arrays, such as arrays of photodiodes. In these embodiments, each detector array may include 4 or more detectors, such as 10 or more detectors, such as 25 or more detectors, such as 50 or more detectors, such as 100 or more detectors, such as 250 or more detectors, such as 500 or more detectors, such as 750 or more detectors, and including 1000 or more detectors. For example, the detector may be a photodiode array having 4 or more photodiodes, such as 10 or more photodiodes, such as 25 or more photodiodes, such as 50 or more photodiodes, such as 100 or more photodiodes, such as 250 or more photodiodes, such as 500 or more photodiodes, such as 750 or more photodiodes, and including 1000 or more photodiodes.

[0101] The detectors can be arranged in any geometric shape as desired, where arrangements of interest include, but are not limited to, square configurations, rectangular configurations, trapezoidal configurations, triangular configurations, hexagonal configurations, heptagonal configurations, octagonal configurations, nonagonal configurations, decagonal configurations, dodecagonal configurations, circular configurations, elliptical configurations, and irregularly patterned configurations. The detectors in the detector array can be oriented relative to each other (with the XZ plane as a reference) at angles ranging from 10° to 180°, such as from 15° to 170°, such as from 20° to 160°, such as from 25° to 150°, such as from 30° to 120°, and including from 45° to 90°. The detector array can be of any suitable shape, and can be rectilinear (e.g., square, rectangular, trapezoidal, triangular, hexagonal, etc.), curved (e.g., circular, elliptical), and irregular (e.g., a parabola with a bottom coupled to a top of a plane). In some embodiments, the detector array has a rectangular effective surface.

[0102] In some embodiments, the particle analyzer includes one or more wavelength separators that are located between the flow cell and the particle modulated light detector. The term "wavelength separator" is used in its conventional sense herein to refer to an optical element configured to separate light collected from a sample into a predetermined spectral range. In some embodiments, the particle analyzer includes a single wavelength separator. In other embodiments, the particle analyzer includes a plurality of wavelength separators, such as 2 or more wavelength separators, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, such as 25 or more, such as 50 or more, such as 75 or more, and including 100 or more wavelength separators. In some embodiments, the wavelength separator is configured to separate light collected from a sample into a predetermined spectral range by allowing light with a predetermined spectral range to pass through and reflecting light of one or more remaining spectral ranges. In other embodiments, the wavelength separator is configured to separate the light collected from the sample into a predetermined spectral range by allowing light having a predetermined spectral range to pass through and absorbing light of one or more remaining spectral ranges. In still other embodiments, the wavelength separator is configured to spatially diffract the light collected from the sample into a predetermined spectral range. Each wavelength separator can be any convenient light separation protocol, such as one or more dichroic mirrors, bandpass filters, diffraction gratings, beam splitters, or prisms. In some embodiments, the wavelength separator is a prism. In other embodiments, the wavelength separator is a diffraction grating. In certain embodiments, the wavelength separator in the present light detection system is a dichroic mirror.

[0103] In some cases, one or more detectors in the system can be considered a trigger sensor (i.e., a sensor that observes the presence of a particle and generates a trigger signal). In some embodiments, the trigger sensor is a forward scattered light detector (e.g., as described above). In other cases, the trigger sensor is an axial light loss (ALL) channel sensor. In this case, the processor can be configured to calculate a trigger window based on the trigger signal, wherein the trigger window provides a time period during which the particle is expected to pass through the detection area of the detector and obtain a baseline noise level during the time period outside the trigger window.

[0104] Suitable flow cytometry systems may include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo et al. (2012) Ann Clin Biochem. Jan; 49(pt 1): 17-28; Linden et al., Semin Throm Hemost, 2004 Oct; 30(5): 502-11; Alison et al., J Pathol, 2010 Dec; 222(4): 335-344; and Herbig et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosure of which is incorporated herein by reference. In some cases, flow cytometry systems of interest include the BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TM X-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TM Flow cytometer and BD Biosciences FACSCaliburTM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter, BD Biosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, BD Biosciences FACSAria TM II cell sorter, BD Biosciences FACSAria TM III cell sorter, BD Biosciences FACSAria TM Fusion Cell Sorter and BD Biosciences FACSMelody TM Cell sorter, BD Biosciences FACSymphony TM S6 cell sorter, etc.

[0105] In some embodiments, the system is a flow cytometry system, such as described in 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; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9 In some cases, the flow cytometry system of the present invention is a clustered wavelength division (CWD) system. CWD systems are described, for example, in U.S. Patent Application Publication No. 2021 / 0247293; the disclosure of which is incorporated herein by reference in its entirety.

[0106] In some cases, the flow cytometry systems of the present invention are configured to image particles in a flowing stream by fluorescence imaging using radiofrequency labeled emission (FIRE), such as those described by Diebold et al. in Nature Photonics Vol. 7(10); 806-810 (2013) and 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; 10,451,538; 10,620,111; and those described in 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.

[0107] Figure 6A system 600 for flow cytometry according to an exemplary embodiment of the present invention is shown. The system 600 includes a flow cytometer 610, a controller / processor 690, and a memory 695. The flow cytometer 610 includes one or more excitation lasers 615a-615c, a focusing lens 620, a flow chamber 625, a forward scatter detector 630, a side scatter detector 635, a fluorescence collection lens 640, one or more beam splitters 645a-645g, one or more bandpass filters 650a-650e, one or more longpass ("LP") filters 655a-655b, and one or more fluorescence detectors 660a-660f.

[0108] The excitation lasers 615a-c emit light in the form of laser beams. Figure 6 In the example system, the wavelengths of the laser beams emitted from the excitation lasers 615a-615c are 488nm, 633nm, and 325nm, respectively. The laser beams are first directed through one or more beam splitters 645a and 645b. Beam splitter 645a transmits 488nm light and reflects 633nm light. Beam splitter 645b transmits UV light (light with a wavelength range of 10 to 400nm) and reflects 488nm and 633nm light.

[0109] The laser beam is then directed to a focusing lens 620, which focuses the beam onto the portion of the sample within the fluid stream within a flow cell 625 where the particles are located. The flow cell is the portion of the fluidics system that directs the particles (usually one at a time) in a stream toward the focused laser beam for detection. The flow cell can include a flow cell in a benchtop cytometer or a nozzle tip in an airflow cytometer.

[0110] Light from the laser beam interacts with particles in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various wavelengths depending on the characteristics of the particles (such as their size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present within the particles). The fluorescence emission, as well as the diffracted, refracted, reflected, and scattered light, can be routed to one or more of a forward scatter detector 630, a side scatter detector 635, and one or more fluorescence detectors 660a-660f via one or more of beam splitters 645c-645g, bandpass filters 650a-650e, longpass filters 655a-655b, and a fluorescence collection lens 640.

[0111] Fluorescence collection lens 640 collects light emitted from the particle-laser beam interaction and routes the light toward one or more beam splitters and filters. Bandpass filters (such as bandpass filters 650a-650e) allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 650a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number represents the range of the spectral band. Therefore, a 510 / 20 filter extends 10nm on each side of the center of the spectral band, or from 500nm to 520nm. Shortpass filters transmit wavelengths of light equal to or shorter than a specific wavelength. Longpass filters (such as longpass filters 655a-655b) transmit wavelengths of light equal to or longer than a specific wavelength. For example, longpass filter 655b (which is a 670nm longpass filter) transmits light equal to or longer than 670nm. Filters are typically selected to optimize the specificity of the detector for a particular fluorescent dye. The filter can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0112] The position of the forward scattering detector 630 is slightly offset from the axis of the direct light beam passing through the flow cell and is configured to detect diffracted light, that is, excitation light that passes through or bypasses the particles mainly in the forward direction. The light intensity detected by the forward scattering detector depends on the overall size of the particles. The forward scattering detector may include a photodiode. The side scattering detector 635 is configured to detect refracted and reflected light from the surface and internal structure of the particles, which increases with the increase in the complexity of the particle structure. The fluorescence emitted from the fluorescent molecules associated with the particles can be detected by one or more fluorescence detectors 660a-660f. The side scattering detector 635 and the fluorescence detector may include a photomultiplier tube. The signals detected by the forward scattering detector 630, the side scattering detector 635 and the fluorescence detector can be converted into electronic signals (voltage) by the detector. These data can provide information about the sample.

[0113] Those skilled in the art should realize that the flow cytometer according to the embodiment of the present invention is not limited to Figure 6 The flow cytometer shown, rather, can include any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors at a variety of wavelengths and in a variety of different configurations.

[0114] During operation, the cytometer's operation is controlled by a controller / processor 690, and measurement data from the detectors can be stored in a memory 695 and processed by the controller / processor 690. Although not explicitly shown in the figure, the controller / processor 690 is coupled to the detectors to receive their output signals, and can also be coupled to the electrical and electromechanical components of the flow cytometer 610 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 697 can also be provided in the system. The memory 695, controller / processor 690, and I / O 697 can be provided entirely as an integral part of the flow cytometer 610. In such embodiments, a display can also form part of the I / O functionality 697 for presenting experimental data to a user of the cytometer 610. Alternatively, some or all of the memory 695, controller / processor 690, and I / O functionality can be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 695 and controller / processor 690 can communicate with the cytometer 610 wirelessly or by wire. The controller / processor 690, along with the memory 695 and I / O 697, may be configured to perform various functions related to the preparation and analysis of flow cytometric experiments.

[0115] Figure 6 The illustrated system includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as the "filter windows" of a given detector), defined by the configuration of filters and / or spectrometers in the beam path from the flow cell 625 to each detector. Different fluorescent molecules used in a flow cytometer experiment will emit light in their respective characteristic wavelength bands. The specific fluorescent labels used in the experiment and their associated fluorescence emission wavelength bands can be selected to roughly coincide with the filter windows of the detectors. The I / O 697 can be configured to receive data related to a flow cytometer experiment that includes a set of fluorescent labels and a plurality of cell populations having the plurality of labels, each cell population having a subset of the plurality of labels. The I / O 697 can also be configured to receive biological data assigning one or more labels to one or more cell populations, label density data, emission spectral data, data assigning labels to one or more labels, and cytometer configuration data. Flow cytometer experiment data (such as label spectral characteristics and flow cytometer configuration data) can also be stored in the memory 695. The controller / processor 690 can be configured to evaluate one or more assignments of labels to labels.

[0116] In some embodiments, the system is a particle sorting system configured to sort particles using a closed particle sorting module, such as the system described in U.S. Patent Publication No. 2017 / 0299493, filed on March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting decision module having multiple sorting decision units, such as described in U.S. Patent Publication No. 2020 / 0256781, filed on December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, a system for sorting components of a sample includes a particle sorting module having a deflection plate, such as the system described in U.S. Patent Publication No. 2017 / 0299493, filed on March 28, 2017, the disclosure of which is incorporated herein by reference.

[0117] Figure 7 A functional block diagram of an example of a system is shown, which has a processor 700 for analyzing and displaying biological events. The processor 700 can be configured to implement various processes for controlling the graphical display of biological events. A flow cytometer or sorting system 702 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data (e.g., particle modulated light data). The flow cytometer 702 can be configured to provide biological event data to the processor 700. A data communication channel can be included between the flow cytometer 702 and the processor 700. The biological event data can be provided to the processor 700 via the data communication channel.

[0118] The processor 700 can be configured to receive biological event data from the flow cytometer 702. The biological event data received from the flow cytometer 702 can include flow cytometric event data. The processor 700 can be configured to evaluate the data received from the flow cytometer 702, for example, as described above. The processor 700 can be configured to perform the method, for example, by receiving a secondary dataset comprising data points collected from a secondary sample (optionally via a flow cytometer or sorting system 702) and calculating a second dimensionality reduction of the secondary dataset based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample.

[0119] The processor 700 can be configured to provide a graphical display including bio-event data to the display device 706. For example, the processor 700 can provide the ideal gains calculated for each detector in the system (e.g., fluorescence detectors 360a-e) to the graphical display device 706. These ideal gains can be output, for example, in the form of tube target values (TTVs). The processor 700 can also be configured to render a region of interest as a gate around the bio-event data group displayed by the display device 706, for example, superimposed on the first graph. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest plotted on a single parameter histogram or a bivariate graph. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0120] The processor 700 may also be configured to display the bio-event data within the portal on the display device 706 in a manner different from other events in the bio-event data outside the portal. For example, the processor 700 may be configured to render the bio-event data within the portal in a different color than the bio-event data outside the portal. The display device 706 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0121] The processor 700 can be configured to receive a door selection signal from a first input device for identifying a door. For example, the first input device can be implemented as a mouse 710. The mouse 710 can send a door selection signal to the processor 700 for identifying a door to be displayed on the display device 706 or to be manipulated via the display device (for example, by clicking on the door when the cursor is at the desired door). In some implementations, the first device can be implemented as a keyboard 708 or other device for providing input signals to the processor 700, such as a touch screen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple input functions. In these implementations, the input functions can be respectively regarded as one input device. For example, Figure 7 As shown, the mouse 710 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0122] A triggering event may cause the processor 700 to change the manner in which the data is displayed, the portion of the data actually displayed on the display device 706, and / or provide input for further processing (such as selecting a population of interest for particle sorting).

[0123] In some embodiments, the processor 700 can be configured to detect when the mouse 710 initiates a gate selection. The processor 700 can also be configured to automatically modify the plot visualization to facilitate the gating process. The modification can be based on the specific distribution of the bio-event data received by the processor 700. In some embodiments, the processor 700 expands the first gate so that a second gate is generated (e.g., as described above).

[0124] Processor 700 may be connected to storage device 704. Storage device 704 may be configured to receive and store biological event data from processor 700. Storage device 704 may also be configured to receive and store flow cytometry event data from processor 700. Storage device 704 may also be configured to allow processor 700 to retrieve biological event data, such as flow cytometry event data.

[0125] The display device 706 can be configured to receive display data from the processor 700. The display data can include a graph of biological event data and gates that outline portions of the graph. The display device 706 can also be configured to change the information presented based on input received from the processor 700 and input from the flow cytometer 702, the storage device 704, the keyboard 708, and / or the mouse 710.

[0126] In some implementations, the processor 700 may generate a user interface to receive example events for classification. For example, the user interface may include a mechanism for receiving example events or example images. The example events or images or example gates may be provided prior to event data collection for the sample, or may be provided based on an initial set of events for a portion of the sample.

[0127] Figure 8A is a schematic diagram of a particle sorting system 800 (e.g., flow cytometer 702) according to one embodiment described herein. In some embodiments, the particle sorting system 800 is a cell sorting system. Figure 8A As shown, a droplet formation transducer 802 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 801, which can be coupled to, include, or be a nozzle 803. Within the fluid conduit 801, a sheath fluid 804 hydrodynamically focuses a sample fluid 806 including particles 809 into a moving fluid column 808 (e.g., a stream). Within the moving fluid column 808, the particles 809 (e.g., cells) are aligned in a single file to pass through a monitoring region 811 (e.g., where the laser streams intersect) illuminated by an illumination source 812 (e.g., a laser). The vibrations of the droplet formation transducer 802 cause the moving fluid column 808 to break into a plurality of droplets 810, some of which contain particles 809.

[0128] In operation, a detection station 814 (e.g., an event detector) identifies when a particle of interest (or cell of interest) passes through the monitoring area 811. The detection station 814 feeds a signal into a timing circuit 828, which in turn feeds a signal into a flash charge circuit 830. At the droplet breakoff point, informed by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 808 such that the droplet of interest is charged. The droplet of interest may include one or more particles or cells to be sorted. The charged droplets may then be sorted by activating a deflection plate (not shown) to deflect the droplet into a container (such as a collection tube or a multi-well or micro-well sample plate), where the wells or micro-wells may be associated with the particular droplet of interest. As Figure 8A As shown, the droplets can be collected in the drainage container 838.

[0129] A detection system 816 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitoring region 811. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 816 enables the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 816 can be fed with an amplitude signal 820 and / or a phase signal 818, which in turn are fed (via an amplifier 822) into an amplitude control circuit 826 and / or a frequency control circuit 824. The amplitude control circuit 826 and / or the frequency control circuit 824, in turn, control the droplet forming transducer 802. The amplitude control circuit 826 and / or the frequency control circuit 824 can be included in the control system.

[0130] In some embodiments, the sorting electronics (e.g., detection system 816, detection station 814, and processor 840) can be coupled to a memory configured to store detected events and sorting decisions based on the events. The sorting decision can be included in the event data for the particle. In some embodiments, the detection system 816 and detection station 814 can be implemented as a single detection unit, or can be communicatively coupled such that event measurements can be collected by one of the detection system 816 or detection station 814 and provided to a non-collecting component.

[0131] Figure 8B is a schematic diagram of a particle sorting system according to one embodiment described herein. Figure 8B The particle sorting system 800 shown includes deflection plates 852 and 854. A charge can be applied via streamer wires in the barbs. This produces a stream of droplets 810 containing particles 809 for analysis. The particles can be illuminated using one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The particle information can be obtained, for example, by sorting electronics or other detection systems ( Figure 8BDeflector plates 852 and 854 can be independently controlled to attract or repel charged droplets, thereby directing the droplets toward a target collection container (e.g., one of 872, 874, 876, or 878). Figure 8B As shown, deflector plates 852 and 854 can be controlled to direct particles along a first path 862 toward a container 874, or along a second path 868 toward a container 878. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particles to continue flowing along flow path 864. Such uncharged droplets can be directed to a waste container, such as via an aspirator 870.

[0132] Sorting electronics may be included to initiate acquisition of measurement data, receive fluorescence signals from particles, and determine how to adjust the deflection plates to sort the particles. Figure 8B Example implementations of the illustrated embodiment include the BD FACSAria commercially available from Becton Dickinson, Inc. (Franklin Lakes, NJ). TM Serial flow cytometer.

[0133] In certain embodiments, as Figure 9As shown, the system is a fluorescence imaging system using a particle sorter that supports radio frequency tag emission imaging. Particle sorter 900 includes an optical illumination assembly 900a, which includes a light source 901 (e.g., a 488 nm laser) that generates an output beam 901a, which is split into beams 902a and 902b using a beam splitter 902. Beam 902a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 903 to generate an output beam 903a having one or more angularly deflected beams. In some cases, output beam 903a generated by acousto-optic device 903 includes a local oscillator beam and multiple radio frequency comb beams. Beam 902b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 904 to generate an output beam 904a having one or more angularly deflected beams. In some cases, output beam 904a generated from acousto-optic device 904 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 903a and 904a generated by the acousto-optic devices 903 and 904, respectively, are combined with a beam splitter 905 to generate an output beam 905a, which is transmitted through an optical component 906 (e.g., an objective lens) to illuminate the particles in the flow cell 907. In some embodiments, the acousto-optic device 903 (AOD) splits a single laser beam into an array of sub-beams, each with a different optical frequency and angle. A second AOD 904 adjusts the optical frequency of a reference beam, which then overlaps with the array of sub-beams at the beam combiner 905. In some embodiments, the light illumination system having a light source and an acousto-optic device can also include the system described in Schraivogel et al., "High-speed fluorescence image-enabled cell sorting," Science (2022), 375(6578): 315-320 and U.S. Patent Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0134] Output beam 905a illuminates sample particles 908 propagating through flow cell 907 (e.g., with sheath fluid 909) at illumination region 910. As shown in illumination region 910, multiple beams (e.g., angularly deflected RF-shifted beams depicted as dots on illumination region 910) overlap with a reference LO beam (depicted as hatched lines on illumination region 910). Due to the different optical frequencies of the overlapping beams, they exhibit beat frequency behavior, which causes each beamlet to emit light at a different frequency f. 1-n Perform sinusoidal modulation.

[0135] Light from the irradiated sample is transmitted to a light detection system 900b comprising a plurality of photodetectors. The light detection system 900b includes a forward scattered light photodetector 911 for generating a forward scattered image 911a and a side scattered light photodetector 912 for generating a side scattered image 912a. The light detection system 900b also includes a bright field photodetector 913 for generating a light loss image 913a. In some embodiments, the forward scattered detector 911 and the side scattered detector 912 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright field photodetector 913 is a photomultiplier tube (PMT). Fluorescence from the irradiated sample is also detected using fluorescence photodetectors 914-917. In some cases, the photodetectors 914-917 are photomultiplier tubes. The light from the irradiated sample is guided to the side scattered detection channel 912 and the fluorescence detection channels 914-917 through a beam splitter 920. Light detection system 900b includes bandpass optical elements 921, 922, 923, and 924 (e.g., dichroic mirrors) for directing light of predetermined wavelengths to photodetectors 914-917. In some cases, optical element 921 has a 534nm / 40nm bandpass. In some cases, optical element 922 has a 586nm / 42nm bandpass. In some cases, optical element 923 has a 700nm / 54nm bandpass. In some cases, optical element 924 has a 783nm / 56nm bandpass. The first number indicates the center of the spectral band. The second number provides the extent of the spectral band. Thus, a 510 / 20 filter extends 10nm on each side of the center of the spectral band, or from 500nm to 520nm.

[0136] Data signals generated in response to light detected in scattered light detection channels 911 and 912, bright field light detection channel 913, and fluorescence detection channels 914-917 are processed by real-time digital processing using processors 950 and 951. Images 911a-917a can be generated in each light detection channel based on the data signals generated in processors 950 and 951. Image-enabled sorting is performed in response to a sorting signal generated in a sorting trigger 952. The sorting assembly 900c includes a deflection plate 931 for deflecting particles into a sample container 932 or a waste stream 933. In some cases, the sorting assembly 900c is configured to sort particles using a closed particle sorting module, such as the module described in U.S. Patent Publication No. 2017 / 0299493, filed on March 28, 2017, the disclosure of which is incorporated herein by reference. In some embodiments, the sorting component 900c includes a sorting decision module having multiple sorting decision units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0137] Non-transitory computer-readable storage medium

[0138] Aspects of the present disclosure also include non-transitory computer-readable storage media having instructions for practicing the present method. The computer-readable storage medium can be used on one or more computers for full or partial automation of the system for practicing the methods described herein. In certain embodiments, the instructions according to the methods described herein can be encoded in the form of "programming" onto a computer-readable medium, where the term "computer-readable medium" as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state drives, flash drives, and network-attached storage (NAS), whether these devices are located inside or outside the computer. Files containing information can be "stored" on a computer-readable medium, where "storage" refers to recording information so that the computer can access and retrieve it later. The computer-implemented methods described herein can be executed using programs written in one or more of any number of computer programming languages. These languages include, for example, Java, Python, Visual Basic, and C++, as well as many other languages.

[0139] In some embodiments, a computer-readable storage medium of interest includes a computer program stored thereon, wherein the computer program, when loaded onto a computer, includes instructions for implementing the method of the present invention as described herein, namely, receiving a secondary dataset comprising data points collected from a secondary sample, and computing a second dimensionality reduction of the secondary dataset based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample. The second dimensionality reduction computed by the computer-readable storage medium of interest is compatible with the reduced reference dataset.

[0140] Computer-controlled systems

[0141] Aspects of the present disclosure also include computer-controlled systems, wherein the system includes one or more computers for full or partial automation. In some embodiments, the system includes a computer having a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when loaded onto the computer, includes instructions for implementing the method of the present invention as described herein, namely, receiving a secondary dataset comprising data points collected from a secondary sample, and calculating a second dimensionality reduction of the secondary dataset based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample. The calculated second dimensionality reduction is compatible with the reduced reference dataset.

[0142] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, or the like. The processing module includes a processor that can access a memory having stored thereon instructions for executing the steps of the method of the present invention. 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, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of several other processors currently available or to be released. The processor executes the operating system, which interfaces with the firmware and hardware in a well-known manner and assists the processor in coordinating and executing the functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, all of which are known in the art. The operating system typically collaborates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, communication control, and related services, all of which are based on known techniques. In some embodiments, the processor includes analog electronics that provide feedback control (such as, for example, negative feedback control).

[0143] The system memory can be any of various known or future memory storage devices. Examples include any common random access memory (RAM), magnetic media (such as a resident hard disk or tape), optical media (such as a readable and writable optical disk), flash memory device, or other memory storage device. The memory storage device can be any of various known or future devices, including an optical disk drive, a tape drive, or a floppy disk drive. Such memory storage devices typically read from and / or write to a program storage medium (not shown), such as, for example, an optical disk, from a program storage medium (not shown). Any of these program storage media, or other program storage media currently in use or that may be developed in the future, can be considered a computer program product. It should be understood that 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 the memory storage device.

[0144] In some embodiments, a computer program product is described that includes a computer-usable medium having control logic (a 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 primarily implemented in hardware, for example using a hardware state machine. Those skilled in the relevant art will understand how to implement a hardware state machine to perform the functions described herein.

[0145] The memory may be any suitable device in which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk or tape or RAM, or any other suitable fixed or portable device). The processor may comprise a general-purpose digital microprocessor suitably programmed by a computer-readable medium carrying the necessary program code. The program may be provided to the processor remotely via a communication channel, or pre-stored in a computer program product (such as a memory or some other portable or fixed computer-readable storage medium) and using any of these devices associated with the memory. For example, a magnetic or optical disk may carry the program and be read by a disk writer / reader. The system of the present invention also includes an algorithm, for example, written in the form of a computer program product, for implementing the method described above. The program according to the present invention may be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and hybrids of these categories, such as magnetic / optical storage media.

[0146] The processor may also access a communication channel to communicate with a user at a remote location, where the user does not interact directly with the system but instead 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 (e.g., a smartphone).

[0147] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Communication protocols and cellular communications, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0148] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., physical ports or interfaces such as a USB port, a USB-C port, an RS-232 port, or any other suitable electrical connection port, to allow data communication between the system and other external devices, such as a computer terminal configured for similar complementary data communications (e.g., in a doctor's office or hospital environment).

[0149] In one embodiment, the communication interface is configured for infrared communication, communications or any other suitable wireless communication protocol to enable the 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 the system.

[0150] In one embodiment, the communication interface is configured to provide a connection for data transmission using Internet Protocol (IP) through 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 Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0151] In one embodiment, the system is configured to use, for example, a common standard such as 802.11 or The server device wirelessly communicates with the server device via a communication interface using a radio frequency protocol (RF protocol, or IrDA infrared protocol). The server device can be another portable device, such as a smartphone, a personal digital assistant (PDA), or a laptop; or a larger device, such as a desktop computer or a home appliance. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0152] In some embodiments, the communication interface is configured to automatically or semi-automatically transfer data stored in the system (eg, the optional data storage unit) to and from a network or server device using one or more of the above-described communication protocols and / or mechanisms.

[0153] 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, whether local or remote). If one of the display devices provides visual information, the information may typically be logically and / or physically organized as an array of graphic elements. A 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. In alternative embodiments, some of these communications may be implemented 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, such as via the Internet, telephone, or satellite network, in accordance with known techniques. The data presentation 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 other SQL, HTML, XML, or other documents or data from a remote source. The one or more platforms present in this system may be any type of known computer platform or one to be developed in the future, although they will generally fall into the category of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other types of computers. They may be connected by any known or future type of cables or other communication systems (including wireless systems), whether networked or otherwise. They may be co-located, or they may be physically separate. A variety of operating systems may be used on any computer platform, which may depend on the type and / or brand of computer platform selected. Suitable operating systems include XP, 7. 8. 10. Android TM 、SGI Oracle wait.

[0154] Figure 10 Depicted is the overall architecture of an example computing device 1000 in accordance with certain embodiments. Figure 10The overall architecture of the computing device 1000 shown includes the arrangement of computer hardware and software components. However, in order to provide achievable disclosure, it is not necessarily necessary to show all of these generally conventional elements. As shown, the computing device 1000 includes a processing unit 1010, a network interface 1020, a computer-readable medium drive 1030, an input / output device interface 1040, a display 1050 and an input device 1060, all of which can communicate with each other through a communication bus. The network interface 1020 can provide a connection to one or more networks or computing systems. Therefore, the processing unit 1010 can receive information and instructions from other computing systems or services through the network. The processing unit 1010 can also communicate with the memory 1070 and further provide output information to the optional display 1050 through the input / output device interface 1040. For example, analysis software (for example, such as FlowJo) stored as executable instructions in the non-temporary memory of the analysis system TM The input / output device interface 1040 can also accept input from an optional input device 1060, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope, or other input device.

[0155] Memory 1070 may include computer program instructions (in some embodiments, these instructions are grouped into modules or components), and processing unit 1010 executes these instructions to implement one or more embodiments. Memory 1070 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 1070 may store an operating system 1072, which provides computer program instructions for processing unit 1010 to use for overall management and operation of computing device 1000. Data may be stored in data storage device 1090. Memory 1070 may also include computer program instructions and other information for implementing various aspects of the present disclosure.

[0156] Practicality

[0157] The present methods, systems, and computer-readable media can be used when it is necessary to minimize the need to pre-collect all samples and concatenate them for calculating dimensionality reduction. The present invention provides a workflow in which a user collects one or more reference samples, calculates dimensionality reduction for these sample(s), and uses them as training (reference) to calculate compatible dimensionality reduction for new samples in the future. The present methods, systems, and computer-readable media can also be used when it is necessary to design a quality score to indicate whether the data is sufficiently stable over time.

[0158] Embodiments of the present invention can be used for the application that the cell that prepares from biological sample can be used for research, laboratory test or treatment.In certain embodiments, the method and apparatus of the present invention can promote to obtain individual cells from target fluid or tissue biological sample.For example, the method and system of the present invention promote to obtain cell from fluid or tissue sample, to be used as research or diagnosis sample of disease such as cancer.Equally, the method and system can promote to obtain cell from fluid or tissue sample, to be used for treatment.Compared with traditional flow cytometry system, the method and apparatus of the present disclosure can separate and collect cell from biological sample (such as organ, tissue, tissue fragment, body fluid) with higher efficiency and lower cost.

[0159] Kit

[0160] Aspects of the present disclosure also include kits, wherein the kit includes storage media such as magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network attached storage (NAS). Any of these program storage media, or other program storage media currently in use or that may be developed later, may be included in this kit. In an embodiment, the program storage medium includes instructions for performing dimensionality reduction by the methods described herein. In an embodiment, the instructions or a portion thereof included on the computer-readable medium provided in this kit can be implemented as a software component of software for analyzing data. In these embodiments, the computer-controlled system according to the present disclosure can be used as a software component for existing software packages (e.g., FlowJo TM ) software "plug-in" to run.

[0161] In addition to the above-mentioned components, this suite can also (in certain embodiments) include instructions, such as instructions for installing the plug-in to an existing software package. These instructions can be present in this suite in various forms, wherein one or more forms can be present in the suite. A form in which these instructions can exist is printed information on a suitable medium or substrate, such as one or more sheets of paper with information printed thereon, in the packaging of the suite, in package instructions, etc. Another form of these instructions is a computer-readable medium having information recorded thereon, such as a floppy disk, an optical disc (CD), a portable flash drive, etc. Another form of these instructions that can exist is a website address, which can be used by accessing information at a remote site via the Internet.

[0162] Notwithstanding the appended claims, the present invention may also be defined by the following provisions:

[0163] 1. A computer-implemented dimensionality reduction method, the method comprising, by a processor:

[0164] receiving a secondary data set comprising data points collected from a secondary sample; and

[0165] A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

[0166] 2. The computer-implemented method of clause 1, wherein computing the second dimensionality reduction comprises determining data points collected from the reference sample that are closest to each data point collected from the secondary sample with respect to a given data parameter.

[0167] 3. The computer-implemented method of clause 2, wherein computing the second dimensionality reduction comprises computing k nearest neighbors within the reference data set for each data point collected from the secondary sample.

[0168] 4. The computer-implemented method of clause 3, wherein k ranges from 1 to m, where m is half the number of data points in the reference sample.

[0169] 5. The computer-implemented method of clause 4, wherein k ranges from 2 to 5.

[0170] 6. A computer-implemented method according to any one of clauses 3 to 5, wherein the method comprises computing the k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, hierarchical navigable small worlds, approximate nearest neighbors with random projection trees, a GPU-based KNN search, or a brute-force KNN search.

[0171] 7. The computer-implemented method of any one of clauses 3 to 6, further comprising calculating a distance between each data point collected from the secondary sample and each of the k nearest neighbors.

[0172] 8. The computer-implemented method of clause 7, wherein the distance is Manhattan distance.

[0173] 9. The computer-implemented method of clause 7, wherein the distance is a Euclidean distance.

[0174] 10. The computer-implemented method of clause 7, wherein the distance is a Chebyshev distance.

[0175] 11. The computer-implemented method of clause 7, wherein the distance is a Minkowski distance.

[0176] 12. The computer-implemented method of clause 7, wherein the distance is a cosine distance.

[0177] 13. The computer-implemented method of any of clauses 7 to 12, wherein the method comprises computing the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors.

[0178] 14. The computer-implemented method of clause 13, further comprising calculating a weighted average using a weight obtained based on the distance to each of the k nearest neighbors.

[0179] 15. The computer-implemented method of clause 14, wherein the method comprises obtaining the weights as follows:

[0180]

[0181] in:

[0182] w i is the weight;

[0183] d i is distance; and

[0184] a and b are real numbers.

[0185] 16. The computer-implemented method of clause 14, wherein the method comprises obtaining the weights as follows:

[0186]

[0187] in:

[0188] f is any monotonic completely non-decreasing function;

[0189] w i is the weight;

[0190] d i is distance; and

[0191] a and b are real numbers.

[0192] 17. The computer-implemented method of clause 14, wherein the method comprises obtaining the weights as follows:

[0193]

[0194] in:

[0195] w i is the weight; and

[0196] d i It's distance.

[0197] 18. The computer-implemented method of any preceding clause, wherein the secondary dataset comprises data points associated with a plurality of parameters that match the reference dataset.

[0198] 19. The computer-implemented method of clause 18, further comprising creating derived parameters for the secondary dataset based on parameters of the reduced reference dataset.

[0199] 20. The computer-implemented method of clause 19, wherein the derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter, or a classification parameter.

[0200] 21. The computer-implemented method of clause 20, wherein the derived parameter is a fluorescence parameter.

[0201] 22. The computer-implemented method of any of clauses 19 to 21, wherein creating the derived parameters for the secondary data set comprises linear interpolation.

[0202] 23. The computer-implemented method of any of clauses 7 to 22, further comprising calculating an input quality score indicating how closely each data point collected from the secondary sample correlates with data points within the reference dataset.

[0203] 24. The computer-implemented method of clause 23, wherein calculating the input quality score comprises obtaining a normalized average distance from each data point collected from the secondary sample to k nearest neighbors.

[0204] 25. The computer-implemented method of any of clauses 7 to 24, further comprising computing an output quality score that measures the separation of the k nearest neighbors in the dimensionally reduced first data set.

[0205] 26. The computer-implemented method of clause 25, wherein calculating the output quality score comprises obtaining a normalized average distance for each of the k nearest neighbors within the dimensionality reduced reference dataset.

[0206] 27. The computer-implemented method of any preceding clause, wherein the reference dataset and the secondary dataset comprise flow cytometry data.

[0207] 28. The computer-implemented method of any of the preceding clauses, wherein the reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

[0208] 29. A computer-implemented method according to any of the preceding clauses, wherein the method comprises:

[0209] receiving a reference dataset; and

[0210] Perform the first dimensionality reduction.

[0211] 30. The computer-implemented method of any preceding clause, further comprising transforming the reference dataset and the secondary dataset.

[0212] 31. The computer-implemented method of clause 30, wherein the method comprises transforming the parameters in the reference data set and the parameters in the secondary data set using a linear function, a logarithmic function, a hyperbolic arcsine function, or a bi-exponential function.

[0213] 32. A computer-implemented method according to any of the preceding clauses, wherein the method comprises computing dimensionality reduction of a plurality of secondary data sets comprising data points collected from a plurality of secondary samples.

[0214] 33. A system comprising a processor, the processor comprising a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0215] receiving a secondary data set comprising data points collected from a secondary sample; and

[0216] A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

[0217] 34. The system of clause 33, wherein computing the second dimensionality reduction comprises determining a data point collected from the reference sample that is closest to each data point collected from the secondary sample with respect to a given data parameter.

[0218] 35. The system of clause 34, wherein computing the second dimensionality reduction comprises computing k nearest neighbors within a reference data set for each data point collected from the secondary sample.

[0219] 36. The system of clause 35, wherein k ranges from 1 to m, where m is half the number of data points in the reference sample.

[0220] 37. The system of clause 36, wherein k ranges from 2 to 5.

[0221] 38. A system according to any of clauses 35 to 37, wherein the processor is configured to compute the k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, hierarchical navigable small world, approximate nearest neighbors with random projection trees, a GPU-based KNN search, or a brute-force KNN search.

[0222] 39. A system according to any of clauses 35 to 38, wherein the processor is configured to calculate a distance between each data point collected from the secondary sample and each of the k nearest neighbours.

[0223] 40. The system of clause 39, wherein the distance is Manhattan distance.

[0224] 41. The system of clause 39, wherein the distance is a Euclidean distance.

[0225] 42. The system of clause 39, wherein the distance is a Chebyshev distance.

[0226] 43. The system of clause 39, wherein the distance is a Minkowski distance.

[0227] 44. The system of clause 39, wherein the distance is a cosine distance.

[0228] 45. The system of any of clauses 39 to 44, wherein the processor is configured to compute the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors.

[0229] 46. The system of clause 45, wherein the processor is configured to calculate the weighted average using a weight obtained based on the distance to each of the k nearest neighbors.

[0230] 47. The system of clause 46, wherein the processor is configured to obtain the weights as follows:

[0231]

[0232] in:

[0233] w i is the weight;

[0234] d i is distance; and

[0235] a and b are real numbers.

[0236] 48. The system of clause 46, wherein the processor is configured to obtain the weights as follows:

[0237]

[0238] in:

[0239] f is any monotonic completely non-decreasing function;

[0240] w i is the weight;

[0241] d i is distance; and

[0242] a and b are real numbers.

[0243] 49. The system of clause 46, wherein the processor is configured to obtain the weights as follows:

[0244]

[0245] in:

[0246] w i is the weight; and

[0247] d i It's distance.

[0248] 50. The system of any of clauses 33 to 49, wherein the secondary dataset comprises data points associated with a plurality of parameters that match the reference dataset.

[0249] 51. The system of clause 50, wherein the processor is configured to create derived parameters for the secondary dataset based on parameters of the reduced reference dataset.

[0250] 52. The system of clause 51, wherein the derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter, or a classification parameter.

[0251] 53. The system of clause 52, wherein the derived parameter is a fluorescence parameter.

[0252] 54. The system of any of clauses 51 to 53, wherein creating the derived parameters for the secondary data set comprises linear interpolation.

[0253] 55. The system of any one of clauses 39 to 54, wherein the processor is configured to calculate an input quality score indicating how closely each data point collected from the secondary sample correlates with the data points within the reference dataset.

[0254] 56. The system of clause 55, wherein calculating the input quality score comprises obtaining a normalized average distance from each data point collected from the secondary sample to k nearest neighbors.

[0255] 57. The system of any of clauses 39 to 56, wherein the processor is configured to calculate an output quality score measuring the separation of the k nearest neighbors in the dimensionally reduced first data set.

[0256] 58. The system of clause 57, wherein computing the output quality score comprises obtaining a normalized average distance for each of the k nearest neighbors within the reduced reference dataset.

[0257] 59. A system according to any one of clauses 33 to 58, wherein the processor is in a data receiving relationship with the flow cytometer, and wherein the reference dataset and the secondary dataset both comprise flow cytometer data.

[0258] 60. The system of any of clauses 33 to 59, wherein the reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

[0259] 61. A system according to any of clauses 33 to 60, wherein the processor is configured to:

[0260] receiving a reference dataset; and

[0261] Perform the first dimensionality reduction.

[0262] 62. A system according to any of clauses 33 to 61, wherein the processor is configured to transform the reference data set and the secondary data set.

[0263] 63. The system of clause 62, wherein the processor is configured to transform the parameters in the reference data set and the parameters in the secondary data set using a linear function, a logarithmic function, a hyperbolic arcsine function, or a bi-exponential function.

[0264] 64. A system according to any of clauses 33 to 63, wherein the processor is configured to calculate the dimensionality reduction of a plurality of secondary data sets comprising data points collected from a plurality of secondary samples.

[0265] 65. A non-transitory computer-readable storage medium comprising instructions stored thereon for performing dimensionality reduction by a method comprising the steps of:

[0266] receiving a secondary data set comprising data points collected from a secondary sample; and

[0267] A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

[0268] 66. The non-transitory computer-readable storage medium of clause 65, wherein computing the second dimensionality reduction comprises determining a data point collected from the reference sample that is closest to each data point collected from the secondary sample with respect to a given data parameter.

[0269] 67. The non-transitory computer-readable storage medium of clause 66, wherein computing the second dimensionality reduction comprises computing k nearest neighbors within the reference data set for each data point collected from the secondary sample.

[0270] 68. The non-transitory computer-readable storage medium of clause 67, wherein k ranges from 1 to m, where m is half the number of data points in the reference sample.

[0271] 69. The non-transitory computer-readable storage medium of clause 68, wherein k ranges from 2 to 5.

[0272] 70. A non-transitory computer-readable storage medium according to any one of clauses 67 to 69, wherein the method includes computing k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, hierarchical navigable small worlds, approximate nearest neighbors with random projection trees, a GPU-based KNN search, or a brute-force KNN search.

[0273] 71. The non-transitory computer-readable storage medium of any one of clauses 67 to 70, wherein the method comprises calculating a distance between each data point collected from the secondary sample and each of the k nearest neighbors.

[0274] 72. The non-transitory computer-readable storage medium of clause 71, wherein the distance is a Manhattan distance.

[0275] 73. The non-transitory computer-readable storage medium of clause 71, wherein the distance is a Euclidean distance.

[0276] 74. The non-transitory computer-readable storage medium of clause 71, wherein the distance is a Chebyshev distance.

[0277] 75. The non-transitory computer-readable storage medium of clause 71, wherein the distance is a Minkowski distance.

[0278] 76. The non-transitory computer-readable storage medium of clause 71, wherein the distance is a cosine distance.

[0279] 77. The non-transitory computer-readable storage medium of any one of clauses 71 to 76, wherein the method comprises computing the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors.

[0280] 78. The non-transitory computer-readable storage medium of clause 77, wherein the method further comprises calculating a weighted average using a weight obtained based on a distance to each of the k nearest neighbors.

[0281] 79. The non-transitory computer-readable storage medium of clause 78, wherein the processor is configured to obtain the weights as follows:

[0282]

[0283] in:

[0284] w i is the weight;

[0285] d i is distance; and

[0286] a and b are real numbers.

[0287] 80. The non-transitory computer-readable storage medium of clause 78, wherein the method comprises obtaining the weights as follows:

[0288]

[0289] in:

[0290] f is any monotonic completely non-decreasing function;

[0291] w i is the weight;

[0292] d i is distance; and

[0293] a and b are real numbers.

[0294] 81. The non-transitory computer-readable storage medium of clause 78, wherein the method comprises obtaining the weights as follows:

[0295]

[0296] in:

[0297] w i is the weight; and

[0298] d i It's distance.

[0299] 82. The non-transitory computer-readable storage medium of any of clauses 65 to 81, wherein the secondary dataset comprises data points associated with a plurality of parameters that match the reference dataset.

[0300] 83. The non-transitory computer-readable storage medium of clause 82, wherein the method further comprises creating derived parameters for the secondary dataset based on parameters of the reduced reference dataset.

[0301] 84. The non-transitory computer-readable storage medium of clause 83, wherein the derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter, or a classification parameter.

[0302] 85. The non-transitory computer-readable storage medium of clause 89, wherein the derived parameter is a fluorescence parameter.

[0303] 86. The non-transitory computer-readable storage medium of any of clauses 83 to 85, wherein creating the derived parameters for the secondary data set comprises linear interpolation.

[0304] 87. The non-transitory computer-readable storage medium of any one of clauses 71 to 86, wherein the method further comprises calculating an input quality score indicating how closely each data point collected from the secondary sample correlates with data points within a reference dataset.

[0305] 88. The non-transitory computer-readable storage medium of clause 87, wherein calculating the input quality score comprises obtaining a normalized average distance from each data point collected from the secondary sample to k nearest neighbors.

[0306] 89. The non-transitory computer-readable storage medium of any one of clauses 71 to 88, wherein the method further comprises calculating an output quality score that measures the separation of the k nearest neighbors in the dimensionally reduced first data set.

[0307] 90. The non-transitory computer-readable storage medium of clause 89, wherein calculating the output quality score comprises obtaining a normalized average distance for each of the k nearest neighbors within the reduced reference data set.

[0308] 91. The non-transitory computer-readable storage medium of any one of clauses 65 to 90, wherein the reference dataset and the secondary dataset comprise flow cytometry data.

[0309] 92. The non-transitory computer-readable storage medium of any of clauses 65 to 91, wherein the reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

[0310] 93. A computer-implemented method according to any of clauses 65 to 92, wherein the method comprises:

[0311] receiving a reference dataset; and

[0312] Perform the first dimensionality reduction.

[0313] 94. The non-transitory computer-readable storage medium of any one of clauses 65 to 93, wherein the method further comprises transforming the reference dataset and the secondary dataset.

[0314] 95. A non-transitory computer-readable storage medium according to clause 94, wherein the method includes transforming the parameters in the reference data set and the parameters in the secondary data set using a linear function, a logarithmic function, a hyperbolic inverse sine function, or a bi-exponential function.

[0315] 96. The non-transitory computer-readable storage medium of any one of clauses 65 to 95, wherein the method comprises computing dimensionality reduction of a plurality of secondary data sets comprising data points collected from a plurality of secondary samples.

[0316] The following is presented by way of example only and not limitation.

[0317] experiment

[0318] The flow cytometry data of the first sample (sample S1) and the second sample (sample S2) were obtained from the public FlowRepository dataset FR-FCM-ZYX4. tSNE and UMAP ( Figure 11A ), and use Figure 2-3 The algorithm described in derives compatible tSNE and UMAP for sample S2 ( Figure 11B FS / SS lymphocyte gating was performed on both samples, and the lymphocyte populations were subsequently examined in tSNE and UMAP space as a “sanity check.” Figures 12A-12B A diagram showing matching embeddings using superimposed instructions. Figures 13A-13B A plot showing the matching embeddings using overlays with swapped sample order is shown. Also shown is tSNE ( Figure 14A ) and UMAP( Figure 14B ) calculates the input and output quality scores. Figures 14A-14B As shown, input and output quality scores can be used to gate events with high-quality embeddings. These scores allow users to focus further downstream analysis on data with high-quality embeddings, i.e., events whose embeddings represent a realistic approximation of their ideal positions in the compatible embedding. Specifically, this may not be the case for some events for two main reasons: (i) a similar population of events does not exist in reference sample S1, so the algorithm does not have enough data to place such events in the compatible embedding; or (ii) similar populations of events from reference sample S1 are not consistently positioned in the reduced dimensionality embedding. The former would be indicated by poor input quality scores for such events, while the latter would be indicated by poor output scores for such events. Furthermore, the output quality scores can be used as a quality indicator for the embeddings performed on reference sample S1 using any state-of-the-art dimensionality reduction technique. Specifically, poor output quality scores for a large proportion of events can indicate that the dimensionality reduction performed on reference sample S1 using such state-of-the-art dimensionality reduction techniques is not a faithful representation of the data with respect to maintaining what is often referred to as the local structure of the data, i.e., events that are close to each other in R are not close to each other in D.

[0319] Although the foregoing invention has been described in some detail by way of illustration and example for ease of understanding, it will be readily apparent to one skilled in the art based on the teachings of the present invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0320] Therefore, the foregoing merely illustrates the principles of the present invention. It will be understood by those skilled in the art that, although not explicitly described or illustrated herein, various arrangements can be devised by those skilled in the art that embody the principles of the present invention and are included within its spirit and scope. In addition, all examples and conditional language described herein are primarily intended to help the reader understand the principles of the present invention and the concepts contributed by the inventors to promote the development of the art, and should be understood as not being limited to these specifically described examples and conditions. In addition, all statements herein illustrating the principles, aspects, and embodiments of the present invention and their specific examples are intended to encompass both their structural and functional equivalents. In addition, such equivalents are intended to include currently known equivalents and equivalents developed in the future, i.e., any element developed to perform the same function, regardless of its structure. In addition, regardless of whether such disclosures are explicitly stated in the claims, anything disclosed herein is not intended to be dedicated to the public.

[0321] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention are embodied by the following claims. In the claims, invocation of 112 U.S.C. § 112(f) or 112(6) is expressly defined for use in a limitation in a claim only if the claim recites the precise phrase "means for" or the precise phrase "step for" at the beginning of such limitation; if such precise phrase is not used in a limitation in a claim, 112 U.S.C. § 112(f) or 112(6) is not invoked.

Claims

1. A computer-implemented dimensionality reduction method, the method comprising, by a processor: receiving a secondary data set comprising data points collected from a secondary sample; and A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

2. The computer-implemented method of claim 1 , wherein: Computing the second dimensionality reduction includes determining a data point collected from the reference sample that is closest to each data point collected from the secondary sample with respect to a given data parameter.

3. The computer-implemented method of claim 2, wherein: Computing the second dimensionality reduction includes computing k nearest neighbors within the reference dataset for each data point collected from the secondary sample.

4. The computer-implemented method of claim 3, wherein: k ranges from 1 to m, where m is half the number of data points in the reference sample.

5. The computer-implemented method of claim 4, wherein: k ranges from 2 to 5.

6. The computer-implemented method according to any one of claims 3 to 5, wherein: The method includes computing the k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, a hierarchical navigable small world, an approximate nearest neighbor with a random projection tree, a GPU-based KNN search, or a brute-force KNN search.

7. The computer-implemented method of any one of claims 3 to 6, further comprising calculating a distance between each data point collected from the secondary sample and each of the k nearest neighbors.

8. The computer-implemented method of claim 7, wherein: The distance is the Manhattan distance.

9. The computer-implemented method of claim 7, wherein: The distance is the Euclidean distance.

10. The computer-implemented method of claim 7, wherein: The distance is the Chebyshev distance. The computer-implemented method of claim 7 , wherein the distance is a Minkowski distance.

12. The computer-implemented method of claim 7, wherein the distance is a cosine distance.

13. A computer-implemented method according to any one of claims 7 to 12, wherein the method comprises computing the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbours. 14 . The computer-implemented method of claim 13 , further comprising calculating the weighted average using a weight obtained for each of the k nearest neighbors based on the distance.

15. The computer-implemented method of claim 14, wherein: The method includes obtaining weights as follows: in: w i is the weight; d i is distance; and a and b are real numbers.

16. The computer-implemented method of claim 14, wherein: The method includes obtaining weights as follows: in: f is any monotonic completely non-decreasing function; w i is the weight; d i is distance; and a and b are real numbers.

17. The computer-implemented method of claim 14, wherein: The method includes obtaining weights as follows: in: w i is the weight; and d i It's distance.

18. A computer-implemented method according to any one of the preceding claims, wherein: The secondary dataset includes data points associated with a plurality of parameters that match the reference dataset.

19. The computer-implemented method of claim 18, further comprising creating derived parameters for the secondary dataset based on parameters of the reduced-dimensionality reference dataset.

20. The computer-implemented method of claim 19, wherein: The derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter or a classification parameter.

21. The computer-implemented method of claim 20, wherein: The derived parameter is a fluorescence parameter.

22. The computer-implemented method of any one of claims 19 to 21, wherein: Creating the derived parameters for the secondary data set includes linear interpolation.

23. The computer-implemented method of any one of claims 7 to 22, further comprising calculating an input quality score indicating how closely each data point collected from the secondary sample correlates with the data points within the reference dataset.

24. The computer-implemented method of claim 23, wherein: Calculating the input quality score includes obtaining a normalized average distance from each data point collected from the secondary sample to the k nearest neighbors.

25. The computer-implemented method of any one of claims 7 to 24, further comprising calculating an output quality score that measures the separation of the k nearest neighbors in the dimensionally reduced first data set.

26. The computer-implemented method of claim 25, wherein: Calculating the output quality score includes obtaining a normalized average distance for each of the k nearest neighbors within the reduced-dimensionality reference dataset.

27. A computer-implemented method according to any one of the preceding claims, wherein: The reference dataset and the secondary dataset include flow cytometry data.

28. A computer-implemented method according to any one of the preceding claims, wherein: The reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

29. The computer-implemented method of any preceding claim, wherein: The method comprises: receiving the reference dataset; and The first dimensionality reduction is performed.

30. The computer-implemented method of any preceding claim, further comprising transforming the reference dataset and the secondary dataset.

31. The computer-implemented method of claim 30, wherein: The method includes transforming the parameters in the reference dataset and the parameters in the secondary dataset using a linear function, a logarithmic function, a hyperbolic arcsine function, or a bi-exponential function.

32. A computer-implemented method according to any one of the preceding claims, wherein: The method includes computing dimensionality reduction for a plurality of secondary data sets including data points collected from a plurality of secondary samples.

33. A system comprising a processor, the processor comprising a memory operatively coupled to the processor, wherein: The memory includes instructions stored thereon that, when executed by the processor, cause the processor to: receiving a secondary data set comprising data points collected from a secondary sample; as well as A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

34. The system of claim 33, wherein computing the second dimensionality reduction comprises: A data point acquired from the reference sample that is closest to each data point acquired from the secondary sample with respect to a given data parameter is determined.

35. The system of claim 34, wherein: Calculating the second dimensionality reduction includes calculating k nearest neighbors within the reference data set for each data point collected from the secondary sample.

36. The system of claim 35, wherein: k ranges from 1 to m, where m is half the number of data points in the reference sample.

37. The system of claim 36, wherein: k ranges from 2 to 5.

38. A system according to any one of claims 35 to 37, wherein The processor is configured to compute the k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, hierarchical navigable small worlds, approximate nearest neighbors with random projection trees, a GPU-based KNN search, or a brute-force KNN search.

39. A system according to any one of claims 35 to 38, wherein The processor is configured to calculate a distance between each data point collected from the secondary sample and each of the k nearest neighbors.

40. The system of claim 39, wherein: The distance is the Manhattan distance.

41. The system of claim 39, wherein: The distance is the Euclidean distance.

42. The system of claim 39, wherein: The distance is the Chebyshev distance.

43. The system of claim 39, wherein: The distance is the Minkowski distance.

44. The system of claim 39, wherein: The distance is a cosine distance.

45. A system according to any one of claims 39 to 44, wherein The processor is configured to calculate the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors.

46. The system of claim 45, wherein: The processor is configured to calculate a weighted average using a weight obtained for each of the k nearest neighbors based on the distance.

47. The system of claim 46, wherein: The processor is configured to obtain weights as follows: in: w i is the weight; d i is distance; and a and b are real numbers.

48. The system of claim 46, wherein: The processor is configured to obtain weights as follows: in: f is any monotonic completely non-decreasing function; w i is the weight; d i is distance; and a and b are real numbers.

49. The system of claim 46, wherein: The processor is configured to obtain weights as follows: in: w i is the weight; and d i It's distance.

50. The system of any one of claims 33 to 49, wherein: The secondary dataset includes data points associated with a plurality of parameters that match the reference dataset.

51. The system of claim 50, wherein: The processor is configured to create derived parameters for the secondary dataset based on parameters of the reduced reference dataset.

52. The system of claim 51, wherein: The derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter or a classification parameter.

53. The system of claim 52, wherein: The derived parameter is a fluorescence parameter.

54. A system according to any one of claims 51 to 53, wherein Creating the derived parameters for the secondary data set includes linear interpolation.

55. The system of any one of claims 39 to 54, wherein: The processor is configured to calculate an input quality score indicating how closely each data point collected from the secondary sample correlates with the data points within the reference dataset.

56. The system of claim 55, wherein: Calculating the input quality score includes obtaining a normalized average distance from each data point collected from the secondary sample to the k nearest neighbors.

57. A system according to any one of claims 39 to 56, wherein The processor is configured to calculate an output quality score that measures the separation of the k nearest neighbors in the dimensionally reduced first data set.

58. The system of claim 57, wherein: Calculating the output quality score includes obtaining a normalized average distance for each of the k nearest neighbors within the reduced-dimensionality reference dataset.

59. The system of any one of claims 33 to 58, wherein: The processor is in a data receiving relationship with the flow cytometer, and both the reference dataset and the secondary dataset include flow cytometer data.

60. The system of any one of claims 33 to 59, wherein: The reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

61. The system of any one of claims 33 to 60, wherein: The processor is configured to: receiving the reference dataset; and The first dimensionality reduction is performed.

62. The system of any one of claims 33 to 61, wherein: The processor is configured to transform the reference data set and the secondary data set.

63. The system of claim 62, wherein: The processor is configured to transform the parameters in the reference dataset and the parameters in the secondary dataset using a linear function, a logarithmic function, a hyperbolic arcsine function, or a bi-exponential function.

64. The system of any one of claims 33 to 63, wherein: The processor is configured to compute dimensionality reduction of a plurality of secondary data sets comprising data points collected from a plurality of secondary samples.

65. A non-transitory computer-readable storage medium comprising instructions stored thereon for performing dimensionality reduction by a method comprising the steps of: receiving a secondary data set comprising data points collected from a secondary sample; and A second dimensionality reduction of the secondary dataset is calculated based on a first dimensionality reduction of a reference dataset comprising data points collected from a reference sample, wherein the second dimensionality reduction is compatible with the reduced reference dataset.

66. The non-transitory computer-readable storage medium of claim 65, wherein: Computing the second dimensionality reduction includes determining a data point collected from the reference sample that is closest to each data point collected from the secondary sample with respect to a given data parameter.

67. The non-transitory computer-readable storage medium of claim 66, wherein: Computing the second dimensionality reduction includes computing k nearest neighbors within the reference dataset for each data point collected from the secondary sample.

68. The non-transitory computer-readable storage medium of claim 67, wherein: k ranges from 1 to m, where m is half the number of data points in the reference sample.

69. The non-transitory computer-readable storage medium of claim 68, wherein: k ranges from 2 to 5.

70. The non-transitory computer-readable storage medium of any one of claims 67 to 69, wherein: The method includes computing the k nearest neighbors using a vantage point tree, a k-dimensional tree, a ball tree, a cover tree, locality sensitive hashing, a hierarchical navigable small world, an approximate nearest neighbor with a random projection tree, a GPU-based KNN search, or a brute-force KNN search.

71. The non-transitory computer-readable storage medium of any one of claims 67 to 70, wherein: The method includes calculating a distance between each data point collected from the secondary sample and each of the k nearest neighbors.

72. The non-transitory computer-readable storage medium of claim 71, wherein: The distance is the Manhattan distance.

73. The non-transitory computer-readable storage medium of claim 71, wherein: The distance is the Euclidean distance.

74. The non-transitory computer-readable storage medium of claim 71, wherein: The distance is the Chebyshev distance.

75. The non-transitory computer-readable storage medium of claim 71, wherein: The distance is the Minkowski distance.

76. The non-transitory computer-readable storage medium of claim 71, wherein: The distance is a cosine distance.

77. The non-transitory computer-readable storage medium of any one of claims 71 to 76, wherein: The method includes computing the second dimensionality reduction based on a weighted average of the positions of the k nearest neighbors.

78. The non-transitory computer-readable storage medium of claim 77, wherein: The method further includes calculating a weighted average using a weight obtained for each of the k nearest neighbors based on the distance.

79. The non-transitory computer-readable storage medium of claim 78, wherein: The processor is configured to obtain weights as follows: in: w i is the weight; d i is distance; and a and b are real numbers.

80. The non-transitory computer-readable storage medium of claim 78, wherein: The method includes obtaining weights as follows: in: f is any monotonic completely non-decreasing function; w i is the weight; d i is distance; and a and b are real numbers.

81. The non-transitory computer-readable storage medium of claim 78, wherein: The method includes obtaining weights as follows: in: w i is the weight; and d i It's distance.

82. The non-transitory computer-readable storage medium of any one of claims 65 to 81, wherein: The secondary dataset includes data points associated with a plurality of parameters that match the reference dataset.

83. The non-transitory computer-readable storage medium of claim 82, wherein: The method also includes creating derived parameters for the secondary dataset based on parameters of the reduced reference dataset.

84. The non-transitory computer-readable storage medium of claim 83, wherein: The derived parameter is a fluorescence parameter, a scattering parameter, an imaging parameter or a classification parameter.

85. The non-transitory computer-readable storage medium of claim 89, wherein: The derived parameter is a fluorescence parameter.

86. The non-transitory computer-readable storage medium of any one of claims 83 to 85, wherein: Creating derived parameters for the secondary data set includes linear interpolation.

87. The non-transitory computer-readable storage medium of any one of claims 71 to 86, wherein: The method also includes calculating an input quality score that indicates how closely each data point collected from the secondary sample correlates with the data points within the reference dataset.

88. The non-transitory computer-readable storage medium of claim 87, wherein: Calculating the input quality score includes obtaining a normalized average distance from each data point collected from the secondary sample to the k nearest neighbors.

89. The non-transitory computer-readable storage medium of any one of claims 71 to 88, wherein: The method also includes calculating an output quality score that measures the separation of the k nearest neighbors in the dimensionally reduced first data set.

90. The non-transitory computer-readable storage medium of claim 89, wherein: Calculating the output quality score includes obtaining a normalized average distance for each of the k nearest neighbors within the reduced-dimensionality reference dataset.

91. The non-transitory computer-readable storage medium of any one of claims 65 to 90, wherein: The reference dataset and the secondary dataset include flow cytometry data.

92. The non-transitory computer-readable storage medium of any one of claims 65 to 91, wherein: The reference dataset is a uniformly downsampled reference dataset or a density-based downsampled reference dataset.

93. The computer-implemented method of any one of claims 65 to 92, wherein: The method comprises: receiving the reference dataset; and The first dimensionality reduction is performed.

94. The non-transitory computer-readable storage medium of any one of claims 65 to 93, wherein: The method further comprises transforming the reference data set and the secondary data set.

95. The non-transitory computer-readable storage medium of claim 94, wherein: The method includes transforming the parameters in the reference dataset and the parameters in the secondary dataset using a linear function, a logarithmic function, a hyperbolic arcsine function, or a bi-exponential function.

96. The non-transitory computer-readable storage medium of any one of claims 65 to 95, wherein: The method includes computing dimensionality reduction for a plurality of secondary data sets including data points collected from a plurality of secondary samples.

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