CN116805033A - Information processing system and method for flow cytometer and flow cytometer - Google Patents
Information processing system and method for flow cytometer and flow cytometer Download PDFInfo
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Abstract
The application discloses an information processing method and system for a flow cytometer and the flow cytometer. The information processing method comprises the following steps: detecting the target sample to obtain a plurality of target detection signals; calculating an adjustment matrix; and adjusting the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals. The adjustment matrix is calculated by: dividing a plurality of target detection signals into a first subset and a second subset based on first and second components of the plurality of target detection signals corresponding to first and second detectors, respectively, of two different detectors of the plurality of detectors; the first element of the first and second elements of the adjustment matrix corresponding to the two different detectors is obtained based on the first and second components of the respective target detection signals in the first subset, and the second element is obtained based on the first and second components of the respective target detection signals in the second subset.
Description
Technical Field
The present disclosure relates to the field of flow cytometry, and in particular to an information processing system and method for a flow cytometer and a flow cytometer.
Background
In conventional flow cytometers, a compensation process (also referred to as a "unmixing process") is required to eliminate signal interference between different detection channels to obtain a final detection signal that is used to determine the tag abundance (label abundance) of each unit (e.g., cell, particle, etc.) in the sample. However, the compensation matrix obtained from a single stained sample is often imperfect, especially in the case of high parameter panels. Therefore, further adjustment of the detection signal after compensation based on the compensation matrix is required. Even in a spectrocytometer, a similar adjustment process is sometimes required.
Disclosure of Invention
The following presents a simplified summary of the disclosure in order to provide a basic understanding of some aspects of the disclosure. However, it should be understood that this summary is not an exhaustive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. Its purpose is to present some concepts related to the disclosure in a simplified form as a prelude to the more detailed description that is presented later.
It is an object of the present disclosure to provide an improved information processing system and method for a flow cytometer and a flow cytometer.
According to an aspect of the present disclosure, there is provided an information processing method for a flow cytometer, including: detecting a target sample by the flow cytometer to obtain a plurality of target detection signals; calculating an adjustment matrix; and adjusting the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals. Wherein the adjustment matrix is calculated by: dividing the plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of a plurality of detectors included in the flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors; obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset; and obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset.
According to another aspect of the present disclosure, there is provided an information processing system for a flow cytometer, including a detection signal acquisition unit, an adjustment matrix calculation unit, and a detection signal adjustment unit. The detection signal acquisition unit may be configured to detect a target sample by the flow cytometer to obtain a plurality of target detection signals. The adjustment matrix calculation unit may be configured to calculate the adjustment matrix by: dividing the plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of a plurality of detectors included in the flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors; obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset; and obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset. The detection signal adjustment unit may be configured to adjust the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals.
According to yet another aspect of the present disclosure, there is provided a flow cytometer including the above information processing system.
According to other aspects of the present disclosure, there are also provided a computer program code and a computer program product for implementing the above-described method according to the present disclosure, and a computer readable storage medium having the computer program code recorded thereon for implementing the above-described method according to the present disclosure.
Other aspects of the disclosed embodiments are set forth in the description section below, wherein the detailed description is for fully disclosing preferred embodiments of the disclosed embodiments without placing limitations thereon.
Drawings
The present disclosure may be better understood by reference to the following detailed description taken in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the figures to designate the same or similar components. The accompanying drawings, which are included to provide a further illustration of the preferred embodiments of the disclosure and to explain the principles and advantages of the disclosure, are incorporated in and form a part of the specification along with the detailed description that follows. Wherein:
FIG. 1 is a flowchart showing a flow example of an information processing method for a flow cytometer according to an embodiment of the present disclosure;
fig. 2 is an example showing a logarithmic scatter diagram drawn based on two signal components of a plurality of target detection signals;
fig. 3 is an example showing a linear scatter diagram drawn based on two signal components of a plurality of target detection signals;
FIG. 4 is an example showing a logarithmic scatter plot drawn based on two signal components of an adjusted plurality of target detection signals;
fig. 5 is a block diagram showing a configuration example of an information processing system for a flow cytometer according to an embodiment of the present disclosure; and
fig. 6 is a block diagram showing an example structure of a personal computer that may be employed in the embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure will be described hereinafter with reference to the accompanying drawings. In the interest of clarity and conciseness, not all features of an actual implementation are described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions must be made in order to achieve the developer's specific goals, such as compliance with system-and business-related constraints, and that these constraints will vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure.
It is also noted herein that, in order to avoid obscuring the disclosure with unnecessary details, only the device structures and/or processing steps closely related to the solution according to the present disclosure are shown in the drawings, while other details not greatly related to the present disclosure are omitted.
Embodiments according to the present disclosure are described in detail below with reference to the accompanying drawings.
First, an implementation example of an information processing method for a flow cytometer according to an embodiment of the present disclosure will be described with reference to fig. 1. Fig. 1 is a flowchart showing a flowchart example of a flow of an information processing method 100 for a flow cytometer according to an embodiment of the present disclosure.
As shown in fig. 1, an information processing method 100 according to an embodiment of the present disclosure may start at a start step S102 and end at an end step S110. The information processing method 100 according to the embodiment of the present disclosure may include a detection signal acquisition step S104, an adjustment matrix calculation step S106, and a signal adjustment step S108.
In the detection signal acquisition step S104, a target sample may be detected by a flow cytometer to acquire a plurality of target detection signals. For example, the plurality of target detection signals may correspond to a plurality of events (events), respectively. Each event may correspond to a unit (e.g., cell, particle, etc.) in the target sample. For example, the target sample may include a multi-stained sample, but is not limited thereto, and an appropriate target sample may be selected according to actual needs.
In the adjustment matrix calculation step S106, an adjustment matrix may be calculated by: dividing a plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of a plurality of detectors included in a flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors; obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset; and obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset.
Note that the expression "two different detectors" herein is not limited to a specific two detectors, but may refer to one or more "combinations of two different detectors" corresponding to one or more elements in the adjustment matrix that need to be acquired. The specific meaning of this expression will be better understood in connection with the examples described below with reference to fig. 2 to 4.
For example, the first subset may represent target detection signals corresponding to the following elements in the target sample: the cell has a greater abundance of the tag corresponding to the first detector than the second detector. Accordingly, the second subset may represent target detection signals corresponding to the following units in the target sample: the cell has a tag abundance corresponding to the first detector that is less than the tag abundance corresponding to the second detector.
In the signal adjustment step S108, the plurality of target detection signals may be adjusted based on the adjustment matrix calculated by the adjustment matrix calculation step S106 to obtain adjusted target detection signals.
As an example, in the detection signal acquisition step S104, a plurality of original detection signals obtained by detecting a target sample by a flow cytometer may be compensated with a compensation matrix to obtain a plurality of compensated detection signals as a plurality of target detection signals. For example, the compensation matrix may be one obtained from a single stained sample using existing methods (see, e.g., novo D, gren gori G, rajwa B. Generally unmixing model for multispectral flow Cytometry utilizing nonsquare compensation matrices [ J ]. Cytometry Part A,2013,83 (5): 508-520).
As previously mentioned, in conventional flow cytometers, a compensation process is required to eliminate signal interference between different detection channels to obtain a final detection signal for determining the tag abundance of each cell in the sample. However, the compensation matrix obtained from a single stained sample is often imperfect. Therefore, further adjustment of the detection signal after compensation based on the compensation matrix is required. In the prior art, this adjustment process is performed manually in the display interface of the detection signal. Specifically, the user manually adjusts the overflow parameter by dragging the slider or the like, and if the user feels that the clusters in the scatter diagram appear "straight", the adjustment process ends.
As described above, the information processing method 100 according to the embodiment of the present disclosure may calculate the adjustment matrix and further adjust the compensated detection signal based on the adjustment matrix, so that manual adjustment by a user may be omitted, and convenience may be improved. In addition, since the adjustment process in the prior art depends on the intuitive perception of the user (i.e., whether the clusters are straight), there is a lack of comparability between the results obtained by different users, and the adjusted detection signal may not accurately represent the tag abundance of each cell in the target sample. On the other hand, the information processing method 100 according to the embodiment of the present disclosure does not depend on the intuitive feel of the user, and thus the results obtained by different users can be compared with each other. In addition, the information processing method 100 according to the embodiment of the present disclosure can improve the accuracy of the tag abundance of each unit in the target sample represented based on the adjusted detection signal.
For example, the adjustment matrix calculation step S106 and the signal adjustment step S108 may be automatically performed in response to the acquisition of a plurality of target detection signals in the detection signal acquisition step S104, whereby convenience may be further improved, and data processing efficiency may be significantly improved.
As an example, the plurality of target detection signals acquired in the detection signal acquisition step S104 are a plurality of original detection signals obtained by detecting a target sample by a flow cytometer. That is, the compensation process based on the compensation matrix of the related art may be omitted, and in the signal adjustment step S108, a plurality of original detection signals may be adjusted using the adjustment matrix to obtain a final detection result.
The information processing method 100 according to the embodiment of the present disclosure will be further described below with reference to specific examples.
For example, a plurality of target detection signals obtained by detecting m events by a flow cytometer including n detectors may be used in matrix R m×n And (3) representing. Matrix R m×n May represent a target detection signal corresponding to an event. Matrix R m×n May represent signal components of a plurality of target detection signals obtained by the same detector. For example, fig. 2 shows a signal component R obtained by the ith detector based on a plurality of target detection signals :,i And a signal component R obtained by a j-th detector :,j And an example of a logarithmic scatter plot. In an actual usage scenario, the user tends to view the logarithmic scatter plot. However, in view of the convenience in processing of the linear axis-based scatter plot, the logarithmic scatter plot shown in fig. 2 may be converted into the linear scatter plot shown in fig. 3.
In fig. 2 and 3, each data point corresponds to a target detection signal. Specifically, the abscissa x of data point k k And the ordinate y k The signal components R obtained by the ith detector, which can respectively represent the corresponding target detection signals k,i And a signal component R obtained by a j-th detector k,j 。
For example, the plurality of data points in the scatter plot shown in fig. 3 may be divided into two subsets, namely a first subset and a second subset. As an example, a gaussian mixture model may be employed for subset division. Specifically, two Gaussian models are set, φ (x, y|θ 1 ) And phi (x, y|theta) 2 ) Wherein θ 1 ＝(μ 1 ,Σ 1 ),θ 2 ＝(μ 2 ,Σ 2 ) (see, e.g., https:// blog. Csdn. Net/jinping_shi/arc/details/59613054). Let alpha be 1k And alpha 2k Respectively data points (x k ,y k ) Belongs to model phi (x, y|theta) 1 ) And phi (x, y|theta) 2 ) Wherein alpha is 1k +α 2k =1. The logarithmic function (log-likelihood function) can be represented by the following formula (1):
for example, the Expectation Maximization (EM) method may be employed to maximize log (θ) in equation (1) and based thereon divide the plurality of data points into two subsets, such as the first subset at the upper left as shown in fig. 3And a second subset of the lower right +.>An adjustment matrix S 'may be obtained based on the first subset' n×n Two elements s corresponding to the i-th detector and the j-th detector in (a) j ′ i Sum s i ′ j The first element s in (a) j ′ i And a second element s may be obtained based on the second subset i ′ j . For example, the element s on the diagonal in the matrix is adjusted i ′ i May be set to 1.
For example, the first subset may beIs calculated as a first element s j ′ i . Specifically, the first component of each target detection signal in the first subset may be +.>First component of the respective target detection signals in the first subset as argument +.>And a second component->Performing a linear fit and fitting the slope k thus obtained 1 As a first element s' ji . Similarly, the second subset +.>Is calculated as a second element s 'with respect to the approximate slope of the x-axis' ij . Specifically, the second component of each target detection signal in the second subset may be +.>First component Y as an argument to each target detection signal in the second subset 2 And a second component->Performing a linear fit and fitting the slope k thus obtained 2 As a second element s' ij . By varying the specific values of i and j, an adjustment matrix S 'can be obtained' n×n To be acquired.
The adjusted target detection signal obtained through the signal adjustment step S108 may be represented as R m×n (S′ n×n ) -1 。
In the case of to be used with compensation matrix S n×n For a plurality of original detection signals R m×n Compensation is carried out to obtain multipleA compensated detection signalIn the case of a plurality of target detection signals, the adjusted target detection signal obtained by the signal adjustment step S108 may be expressed as +.>
For example, fig. 4 shows a logarithmic scatter diagram drawn based on the signal component corresponding to the i-th detector and the signal component corresponding to the j-th detector of the adjusted target detection signal obtained through the signal adjustment step S108. Comparing fig. 2 and 4, it can be seen that the adjusted target detection signal well eliminates signal overflow between the detection channels corresponding to the i-th detector and the j-th detector.
As an example, the first subset and the second subset may be processed using a random consensus sampling algorithm (random sample Consensus (RANSAC) algorithm) to remove noise prior to performing the linear fit, thereby further improving the accuracy of the tag abundance of each cell in the target sample represented based on the adjusted detection signal.
Note that while the subset-division of the plurality of target detection signals using the gaussian mixture model is described herein, the subset-division may also be performed using k-means, bayesian, etc. techniques.
The information processing method 100 for a flow cytometer according to the embodiment of the present disclosure has been described above, and the present disclosure also provides the following embodiments of the information processing system for a flow cytometer, corresponding to the embodiments of the information processing method 100 described above. Fig. 5 is a block diagram showing a configuration example of an information processing system 500 for a flow cytometer according to an embodiment of the present disclosure.
For example, as shown in fig. 5, an information processing system 500 according to an embodiment of the present disclosure may include a detection signal acquisition unit 504, an adjustment matrix calculation unit 506, and a detection signal adjustment unit 508.
The detection signal acquisition unit 504 may be configured to detect a target sample by a flow cytometer to obtain a plurality of target detection signals. For example, the target sample may comprise a multi-stained sample. For example, the detection signal acquisition unit 504 may perform the detection signal acquisition step S104 described above, so specific details may be found in the description of the detection signal acquisition step S104 above, and only a brief description will be made below.
The adjustment matrix calculation unit 506 may be configured to calculate the adjustment matrix by: dividing a plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of a plurality of detectors included in a flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors; obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset; and obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset. For example, the adjustment matrix calculation unit 506 may perform the adjustment matrix calculation step S106 described above, so specific details may be found in the description of the adjustment matrix calculation step S106 above, and only a brief description will be made below.
The detection signal adjustment unit 508 may be configured to adjust the plurality of target detection signals based on the adjustment matrix calculated by the adjustment matrix calculation unit 506 to obtain adjusted target detection signals. For example, the detection signal adjusting unit 508 may perform the signal adjusting step S108 described above, so specific details may be found in the description of the signal adjusting step S108 above, and only a brief description will be made below.
As an example, the detection signal acquisition unit 504 may compensate a plurality of original detection signals obtained by detecting a target sample by a flow cytometer using a compensation matrix to obtain a plurality of compensated detection signals as a plurality of target detection signals.
Similar to the above-described information processing method 100, the information processing system 500 according to the embodiment of the present disclosure can omit manual adjustment by a user, improve convenience, and improve data processing efficiency. The adjustment process of the information processing system 500 according to the embodiment of the present disclosure does not depend on the intuitive experience of the user, and thus the results obtained by different users can be compared with each other. In addition, the accuracy of the tag abundance of each cell in the target sample represented based on the adjusted detection signal can also be improved.
As an example, the detection signal acquisition unit 504 may acquire a plurality of original detection signals obtained by detecting a target sample by a flow cytometer as a plurality of target detection signals.
For example, the adjustment matrix calculation unit 506 may take the first component of each target detection signal in the first subset as an argument, perform linear fitting on the first component and the second component of each target detection signal in the first subset, and take the slope thus obtained as the first element. For example, the adjustment matrix calculation unit 506 may take the second component of each target detection signal in the second subset as an argument, perform linear fitting on the first component and the second component of each target detection signal in the second subset, and take the slope thus obtained as the second element.
In addition, according to embodiments of the present disclosure, a flow cytometer including the above-described information processing system 500 may also be provided. For example, the flow cytometer may include, but is not limited to, a spectral flow cytometer.
It should be noted that while the information processing system and method for a flow cytometer and the functional configuration and operation of the flow cytometer according to the embodiments of the present disclosure are described above, this is merely an example and not a limitation, and those skilled in the art may modify the above embodiments according to the principles of the present disclosure, for example, may add, delete or combine functional modules and operations in the respective embodiments, etc., and such modifications fall within the scope of the present disclosure.
It should be noted that the system embodiments herein correspond to the method embodiments described above, and therefore, what is not described in detail in the system embodiments may be referred to the description of the corresponding parts in the method embodiments, and the description is not repeated here.
Further, the present disclosure also provides storage media and program products. It should be appreciated that the machine executable instructions in the storage medium and the program product according to embodiments of the present disclosure may also be configured to perform the above-described information processing method, and thus the contents not described in detail herein may refer to the description of the previous corresponding parts, and the description is not repeated herein.
Accordingly, a storage medium for carrying the above-described program product comprising machine-executable instructions is also included in the disclosure of the present application. Including but not limited to floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.
In addition, it should be noted that the series of processes and systems described above may also be implemented in software and/or firmware. In the case of implementation by software and/or firmware, a program constituting the software is installed from a storage medium or a network to a computer having a dedicated hardware structure, such as the general-purpose personal computer 1000 shown in fig. 6, which is capable of executing various functions and the like when various programs are installed.
In fig. 6, a Central Processing Unit (CPU) 1001 performs various processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1008 to a Random Access Memory (RAM) 1003. In the RAM 1003, data required when the CPU 1001 executes various processes and the like is also stored as needed.
The CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input/output interface 1005 is also connected to the bus 1004.
The following components are connected to the input/output interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output section 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk and the like; and a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet.
The drive 1010 is also connected to the input/output interface 1005 as needed. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 1010 as needed, so that a computer program read out therefrom is installed into the storage section 1008 as needed.
In the case of implementing the above-described series of processes by software, a program constituting the software is installed from a network such as the internet or a storage medium such as the removable medium 1011.
It will be understood by those skilled in the art that such a storage medium is not limited to the removable medium 1011 shown in fig. 6, in which the program is stored, which is distributed separately from the apparatus to provide the program to the user. Examples of the removable medium 1011 include a magnetic disk (including a floppy disk (registered trademark)), an optical disk (including a compact disk read only memory (CD-ROM) and a Digital Versatile Disk (DVD)), a magneto-optical disk (including a Mini Disk (MD) (registered trademark)), and a semiconductor memory. Alternatively, the storage medium may be a hard disk or the like contained in the ROM 1002, the storage section 1008, or the like, in which a program is stored, and distributed to users together with a device containing them.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but the present disclosure is of course not limited to the above examples. Various changes and modifications may be made by those skilled in the art within the scope of the appended claims, and it is understood that such changes and modifications will naturally fall within the technical scope of the present disclosure.
For example, a plurality of functions included in one unit in the above embodiments may be implemented by separate devices. Alternatively, the functions realized by the plurality of units in the above embodiments may be realized by separate devices, respectively. In addition, one of the above functions may be implemented by a plurality of units. Needless to say, such a configuration is included in the technical scope of the present disclosure.
In this specification, the steps described in the flowcharts include not only processes performed in time series in the order described, but also processes performed in parallel or individually, not necessarily in time series. Further, even in the steps of time-series processing, needless to say, the order may be appropriately changed.
Claims (16)
1. An information processing method for a flow cytometer, comprising:
detecting a target sample by the flow cytometer to obtain a plurality of target detection signals;
the adjustment matrix is calculated by:
dividing the plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of the plurality of detectors included in the flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors,
obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset, and
obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset; and
the plurality of target detection signals are adjusted based on the adjustment matrix to obtain adjusted target detection signals.
2. The information processing method according to claim 1, wherein obtaining the first element comprises: performing linear fitting on the first and second components of each target detection signal in the first subset with the first component of each target detection signal in the first subset as an argument, and taking the slope thus obtained as the first element; and
obtaining the second element comprises: and performing linear fitting on the first component and the second component of each target detection signal in the second subset with the second component of each target detection signal in the second subset as an independent variable, and taking the slope obtained thereby as the second element.
3. The information processing method according to claim 1 or 2, wherein the plurality of target detection signals are divided into the first subset and the second subset using a gaussian mixture model.
4. The information processing method according to claim 2, further comprising: the first subset and the second subset are processed using a random consistency sampling algorithm to remove noise prior to performing the linear fit.
5. The information processing method according to claim 1 or 2, wherein obtaining a plurality of target detection signals includes: a plurality of original detection signals obtained by detecting a target sample by the flow cytometer are compensated with a compensation matrix to obtain a plurality of compensated detection signals as the plurality of target detection signals.
6. The information processing method according to claim 1 or 2, wherein a plurality of original detection signals obtained by detecting a target sample by the flow cytometer are used as the plurality of target detection signals.
7. The information processing method according to claim 1 or 2, wherein the target sample comprises a multi-stained sample.
8. An information handling system for a flow cytometer, comprising:
a detection signal acquisition unit configured to detect a target sample by the flow cytometer to obtain a plurality of target detection signals;
an adjustment matrix calculation unit configured to calculate an adjustment matrix by:
dividing the plurality of target detection signals into a first subset and a second subset based on a first component of the plurality of target detection signals corresponding to a first detector of two different detectors of the plurality of detectors included in the flow cytometer and a second component of the plurality of target detection signals corresponding to a second detector of the two different detectors,
obtaining a first element of two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset, and
obtaining a second element of the two elements of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset; and
and a detection signal adjustment unit configured to adjust the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals.
9. The information handling system of claim 8, wherein obtaining the first element comprises: performing linear fitting on the first and second components of each target detection signal in the first subset with the first component of each target detection signal in the first subset as an argument, and taking the slope thus obtained as the first element; and
obtaining the second element comprises: and performing linear fitting on the first component and the second component of each target detection signal in the second subset with the second component of each target detection signal in the second subset as an independent variable, and taking the slope obtained thereby as the second element.
10. The information processing system according to claim 9, wherein the adjustment matrix calculation unit is configured to divide the plurality of target detection signals into the first subset and the second subset using a gaussian mixture model.
11. The information handling system of claim 10, wherein the adjustment matrix calculation unit is configured to process the first subset and the second subset using a random consistency sampling algorithm to remove noise prior to performing the linear fit.
12. The information processing system according to claim 8 or 9, wherein the detection signal acquisition unit is configured to compensate a plurality of original detection signals obtained by detecting a target sample by the flow cytometer using a compensation matrix to obtain a plurality of compensated detection signals as the plurality of target detection signals.
13. The information processing system according to claim 8 or 9, wherein a plurality of original detection signals obtained by detecting a target sample by the flow cytometer are used as the plurality of target detection signals.
14. The information handling system of claim 8 or 9, wherein the target sample comprises a multi-stain sample.
15. A flow cytometer comprising the information handling system of any of claims 8-14.
16. A computer readable storage medium storing instructions which, when executed by a processor, cause the processor to perform the information processing method according to any one of claims 1 to 7.
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