CN113674796B - Method for establishing drug-resistant antibody calculation threshold group and system for realizing method - Google Patents
Method for establishing drug-resistant antibody calculation threshold group and system for realizing method Download PDFInfo
- Publication number
- CN113674796B CN113674796B CN202110944366.4A CN202110944366A CN113674796B CN 113674796 B CN113674796 B CN 113674796B CN 202110944366 A CN202110944366 A CN 202110944366A CN 113674796 B CN113674796 B CN 113674796B
- Authority
- CN
- China
- Prior art keywords
- module
- result
- detection
- outlier
- test
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 56
- 238000000034 method Methods 0.000 title claims abstract description 55
- 239000003814 drug Substances 0.000 title claims abstract description 23
- 229940079593 drug Drugs 0.000 title claims abstract description 23
- 238000001514 detection method Methods 0.000 claims abstract description 76
- 238000012360 testing method Methods 0.000 claims abstract description 67
- 238000004458 analytical method Methods 0.000 claims abstract description 49
- 238000007619 statistical method Methods 0.000 claims abstract description 41
- 238000006243 chemical reaction Methods 0.000 claims abstract description 12
- 230000009466 transformation Effects 0.000 claims abstract description 11
- 238000011156 evaluation Methods 0.000 claims description 14
- RGCLLPNLLBQHPF-HJWRWDBZSA-N phosphamidon Chemical compound CCN(CC)C(=O)C(\Cl)=C(/C)OP(=O)(OC)OC RGCLLPNLLBQHPF-HJWRWDBZSA-N 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000007689 inspection Methods 0.000 claims description 6
- 238000011551 log transformation method Methods 0.000 claims description 4
- 238000000691 measurement method Methods 0.000 abstract description 2
- 239000000523 sample Substances 0.000 description 10
- 230000009897 systematic effect Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 230000005847 immunogenicity Effects 0.000 description 5
- 238000007796 conventional method Methods 0.000 description 3
- 238000013450 outlier detection Methods 0.000 description 3
- 238000000540 analysis of variance Methods 0.000 description 2
- 238000003556 assay Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000001647 drug administration Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000000846 Bartlett's test Methods 0.000 description 1
- 238000001047 Dixon's Q test Methods 0.000 description 1
- 238000001134 F-test Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000012496 blank sample Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000002163 immunogen Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 230000001225 therapeutic effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000003245 working effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
- G16B15/30—Drug targeting using structural data; Docking or binding prediction
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- Pharmacology & Pharmacy (AREA)
- Crystallography & Structural Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Theoretical Computer Science (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention belongs to the technical field of detection and measurement methods, and particularly relates to a method for establishing a calculation threshold group of a drug-resistant antibody and a system for realizing the method. The method comprises the following steps: carrying out logarithmic conversion on the negative signal value on the analysis board, and outputting a detection result; according to the detection result, carrying out logarithmic transformation on the multiple parallel detection results of a single organism, and calculating a residual value; detecting outliers of single biological individuals based on the detection result and the obtained residual error value, and eliminating the outliers; the statistical method is adopted to test the distribution mode of the residual data after outlier removal and output the test result of the distribution mode or output the graph result at the same time; and calculating the Cut-Points value group at the designated false positive rate level according to the obtained distribution mode test result and/or the graph result. The method has universality, can be used for establishing antibody judgment threshold groups in various conditions, can replace the existing complicated operation, and improves the analysis and calculation efficiency.
Description
Technical Field
The invention belongs to the technical field of detection and measurement methods, and particularly relates to a method for establishing a calculation threshold group of a drug-resistant antibody and a system for realizing the method.
Background
Before a Drug is marketed, a series of Drug safety evaluations are required, one of which is to detect the immunogenicity of the Drug, and in popular terms, whether an organism will produce antibodies (i.e., anti-Drug antibodies, ADA for short) against the Drug. The detection of anti-drug antibodies is currently known in the industry to be performed in a manner similar to semi-quantitative, and therefore a decision threshold (Cut Point) needs to be established first, and when the detection value of the sample is higher than this Cut Point value, it is considered that the organism produces anti-drug antibodies.
The set Point value needs to be established by detecting a certain number of normal or healthy organism blank samples to obtain a baseline level of a large sample amount of organisms in a standard state, and in the process, systematic errors possibly generated by detection per se need to be considered among factors of different testers, different experiment time, different analysis plates and the like. And comprehensively summarizing biological differences and systematic errors by using a series of statistical analysis means, and finally obtaining a Cut Point value by statistical analysis.
There are currently literature reports (Devanaryan et al AAPS,2017,Shankar et al,JPBA,2008) or technical guidelines issued by the pharmaceutical monitoring department (pharmaceutical immunogenicity research technical guidelines; national drug administration; FDA guidance on Immunogenicity Testing of Therapeutic Protein Products, 2019) for guiding statistical analysis of Cut Point. In these published documents, a rough Point statistical analysis is recommended, for example, all of variance analysis, data normal distribution test, data conversion, bias test and the like are required.
However, in the statistical analysis process of the Cut Point, what statistical mode and what data conversion mode are adopted do not have clear consensus, and the specific situation that a biological analyzer and a biological statistics scientist are required to comprehensively study is determined. In addition, since the statistical analysis of data involves many steps and the statistical knowledge to be applied is complex, it is difficult for a general biological analyzer to process the data and the data is very prone to errors. Therefore, a software developed specifically for the calculation of immunogenic Cut Point is needed to help bioanalytists to analyze experimental data quickly, accurately and efficiently.
Therefore, it is desirable to provide a method that is versatile, can be easily and quickly used by biological analysts, and can effectively reduce systematic errors associated with different analysis source data.
Disclosure of Invention
In order to solve the problems that the existing process for establishing the calculation threshold value of the anti-drug antibody is complicated, most of the existing biological analysts do not master corresponding statistical skills, errors are easy to occur in the actual calculation process, the efficiency is low, the system error is large and the like, the invention provides a method for establishing the calculation threshold value group of the anti-drug antibody, and a system matched with the method for realizing the method.
The invention aims at:
1. the calculation efficiency and the calculation accuracy can be improved, and the system error is reduced;
2. the calculation mode is simplified, and the biological analysis student without a good statistical calculation basis can use the method conveniently;
3. the system can be formed to realize automatic calculation quickly and effectively.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A method for establishing a threshold population of drug-resistant antibodies,
the method comprises the following steps:
1) Carrying out logarithmic conversion on the negative signal value on the analysis board, and outputting a detection result;
2) According to the detection result of the step 1), carrying out logarithmic transformation on the multiple parallel detection results of the single organism, and calculating residual values of the multiple parallel detection results of the single organism;
3) Detecting an analysis outlier and a biological outlier of a plurality of parallel detection results of a single biological individual based on the detection result of the step 1) and the residual value obtained in the step 2), and removing the analysis outlier and the biological outlier;
4) Adopting a statistical method to test the distribution mode of the residual data after the outlier and the biological outlier are removed and analyzed in the step 3), and outputting a test result of the distribution mode or simultaneously outputting a graph result;
5) Calculating the Cut-Points value group under the appointed false positive rate level according to the distribution mode test result and/or the graph result obtained in the step 4).
In the method, step 1) performs preliminary conversion on the negative signal value, and after conversion, the residual value is calculated in step 2) and the outlier is calculated in step 3), so that systematic errors caused by multiple measurements of single or multiple organisms can be reduced, calculation accuracy is improved, then step 4) obtains actual effective data, the distribution mode of the effective data is checked to obtain a check result, and a graph result corresponding to the check result of the distribution mode is obtained, false positive rate substitution and statistic calculation of a Cut-Points value group performed subsequently can be facilitated, and for parallel tests, the obtained test result and graph result of the distribution mode can be substituted into data for mutual verification and repeated use, unnecessary statistic calculation work can be reduced, work efficiency is improved, final calculation accuracy can be improved, a more accurate Cut-Points value group range is obtained, and a biological analyst can more conveniently and effectively select an optimal Cut-Points value from the obtained Cut-Points value group.
As a preferred alternative to this,
the log10 log transformation is used for the log transformation in step 1).
Log10 logarithmic transformation is straightforward and efficient and is a relatively conventional and versatile way of transformation.
Preferably, step 1) uses a level's Test statistical method to Test the source of and the variance alignment of the different assay plates.
The variance alignment of different analysis boards and sources can be calculated and detected by the level's Test statistical method, so that systematic errors caused by different researchers, different analysis batches and different analysis boards can be reduced.
Compared with other common F-Test statistical methods, bartlett's Test statistical methods and the like, the level's Test statistical method can perform statistical calculation on normal distribution data and perform statistical calculation on non-normal distribution data at the same time, so that universality of the method is improved. Especially, when solving systematic errors caused by personnel, batch and the like, the disorder of data distribution is relatively strong, and the adoption of the level's Test statistical method is the most applicable and proper method.
Preferably, the detection in step 3) is performed by using a k-times bit-distance detection statistical method and/or a Dixon's Test statistical method.
The k times bit distance detection statistical method specifically comprises the following steps: IQR is first set, which is an abbreviation for interquartile range, chinese called quarter-bit distance. One set of samples notes the first quartile as Q1 and the third quartile as Q3, iqr=q3-Q1. In outlier detection, samples less than Q1-kIQR and greater than Q3+kIQR are considered outliers. Typically k takes 1.5, i.e. 1.5 x the quantile, i.e. 1.5 x iqr. Namely, outliers less than Q1-1.5IQR and greater than Q3+1.5IQR in outlier detection.
The Dixon's Test statistical method is also referred to as the Dixon's Q Test statistical method, which is a more common and commonly used method for identifying and rejecting outliers.
Meanwhile, the k times bit distance detection statistical method and the Dixon's Test statistical method are combined with each other, so that the detection accuracy can be actually improved, and the problems that the Dixon's Test statistical method should not be used repeatedly, has limited applicability and the like are effectively solved.
Preferably, the step 4) of checking and outputting the distribution mode checking result is performed by adopting a shape Wilk's statistical method and/or a Shewness Test statistical method.
The Shapiro Wilk's statistical method in the above test method is also called as Shapiro Wilk's W test, which can be applied to normal distribution data test under almost all conditions, has extremely strong universality and good accuracy, and the sample size can even reach 5000, so that the method can be applied to almost all possible test conditions when being applied to the technical scheme of the invention. The Shewness Test statistical method can Test the measurement of the deflection direction and the degree of data distribution, improves the reliability and the comprehensiveness of Test, can effectively realize the Test result of the distribution mode under the condition of combining the two methods, and simultaneously obtains the corresponding graph result.
As a preferred alternative to this,
the graphical result form of step 4) includes at least one of box plot, Q-Q plot, frequency histogram, and density plot.
The above graphical results are all common and conventional well-understood graphical results.
As a preferred alternative to this,
step 5) the appointed false positive rate is 1-5%;
the set-Points value group includes at least two set-Points values.
The above-described false positive rate specification range is a common and commonly used specification range.
A system for implementing the method according to any one of claims 1 to 7, characterized in that,
the system comprises:
the system comprises a signal evaluation module, an individual value preprocessing module, an outlier rejection module, a checking module and a calculation output module;
the signal evaluation module outputs a detection result;
the individual value preprocessing module calculates a data residual value;
the outlier removing module removes and analyzes outliers and biological outliers;
the inspection module outputs a distributed inspection result and/or a graphic result;
and the calculation output module calculates the Cut-Points value group according to the specified false positive rate and the distribution mode test result and/or the graph result.
The system can realize automatic analysis and output a scientific and reasonable result, namely the Cut-Points value group by combining the statistical method and the inspection method used in the method through an algorithm, and the obtained Cut-Points value group is excluded from the system because of the need of manual subjective judgment.
Preferably, the signal evaluation module is used for receiving original information, outputting a detection result and detecting variance alignment, and is embedded with a level's Test statistic calculation sub-module;
the original information comprises a negative signal value and a signal source label;
the signal source tag includes at least one of an analyst and/or an analysis batch;
the signal evaluation module carries out logarithmic transformation on the negative signal value of the received original information to form a detection result and outputs the detection result;
and after the original information is received, the signal evaluation module checks the variance alignment of negative signal values corresponding to different signal source labels on the signal source labels through a level's Test statistical calculation sub-module.
The original information containing signal source labels is set, calculation and analysis of variance uniformity can be carried out, the correction capability of data can be gradually accumulated and formed in the use process of the system through the calculation and analysis of variance uniformity, and the accuracy of calculation and analysis is improved.
Preferably, the individual value preprocessing module receives the detection result output by the signal evaluation module, and calculates the residual value through logarithmic transformation;
the outlier eliminating module is embedded with a k times bit distance detection statistics computation sub-module and/or a Dixon's Test statistics computation sub-module, eliminates and analyzes an outlier and a biological outlier based on a detection result and/or a residual error value, and then outputs a residual detection result to the detection module;
the detection module is embedded with a shape Wilk's statistical computation sub-module and/or a Shewness Test statistical computation sub-module, and the detection module receives the residual detection result output by the outlier rejection module, and then calculates and outputs a distribution mode detection result and/or a graph result by using the shape Wilk's statistical computation sub-module and/or the Shewness Test statistical computation sub-module;
the calculation output module sets false positive rate and substitutes the false positive rate into a distribution mode detection result and/or a graph result to calculate and output a Cut-Points value group.
The modules can greatly improve the working efficiency and the working effect of biological analysis staff by combining a statistical method, and avoid errors caused by manual calculation operation.
The beneficial effects of the invention are as follows:
1) The population of antibody determination thresholds that are universally applicable to a variety of situations is established;
2) The method can replace the existing complicated operation and improve the analysis and calculation efficiency;
3) The obtained calculation result can be ensured to have higher accuracy, and the data is continuously corrected in the process of calculating the variance alignment, so that the system error of analysis and calculation is reduced;
4) The method is simple and efficient, and a biological analysis student can use the method conveniently and rapidly;
5) The system can be formed by combining the modularized design, and automatic analysis and calculation can be realized.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a graph of the results of outliers culling;
FIG. 3 is a graphical result diagram of outliers not culled.
Detailed Description
The invention is described in further detail below with reference to specific examples and figures of the specification. Those of ordinary skill in the art will be able to implement the invention based on these descriptions. In addition, the embodiments of the present invention referred to in the following description are typically only some, but not all, embodiments of the present invention. Therefore, all other embodiments, which can be made by one of ordinary skill in the art without undue burden, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
The raw materials used in the examples of the present invention are all commercially available or available to those skilled in the art unless specifically stated otherwise; the methods used in the examples of the present invention are those known to those skilled in the art unless specifically stated otherwise.
Examples
A system for establishing a calculation threshold group of an anti-drug antibody, a specific system flow chart is shown in fig. 1, and the system is mainly used for realizing the calculation of a judgment threshold group of the anti-drug antibody by the following method:
1) Performing log10 logarithmic transformation on negative signal values on the analysis plates, outputting detection results, and detecting sources of different analysis plates and variance uniformity of different analysis plates by adopting a level's Test statistical method;
2) According to the detection result of the step 1), log10 logarithm conversion is carried out on the multiple parallel detection results of the single organism, and residual values of the multiple parallel detection results of the single organism are calculated;
3) Detecting an analysis outlier and a biological outlier of a single biological individual in a plurality of parallel detection results based on the detection result of the step 1), the variance alignment and the residual error value obtained in the step 2), wherein the detection is carried out by adopting a k-time bit-rate detection statistical method and a Dixon's Test statistical method, and the analysis outlier and the biological outlier are removed after the calculation and the mutual comparison of the two;
4) Adopting a Shapiro Wilk's statistical method and a Shewness Test statistical method to Test the distribution mode of the residual data after rejecting and analyzing the outlier and the biological outlier in the step 3), outputting a distribution mode Test result, and outputting at least one graph result including a box graph, a Q-Q graph, a frequency histogram and a density graph;
5) Calculating a set-Points value group at a false positive rate level of 1-5% according to the distribution mode test result and the graph result obtained in the step 4), wherein the set-Points value group comprises at least two set-Points values.
The algorithm specifically formed is as follows:
step 1) data source input to signal evaluation module:
the data source was 2 analysis plates, named NC and SNR, respectively. Specific assay plate data are shown in tables 1-1 and 1-2. This data was provided by 2 analysts a and b testing, testing a total of 50 samples (S01-S50) on 16 test plates, with multiple test plates made by the same analyst on the same day being defined for 1 analysis batch, involving a total of 4 analysis batches (lot 1-lot 4).
TABLE 1-1NC analysis plate
In Table 1-1: signal is the detection Signal value of NC.
TABLE 1-2SNR analysis panel
In tables 1-2: SNR is the detected value of the sample.
Log10 transformation is carried out on NC signal values of source data, and meanwhile, the detection variance alignment of the submodule is calculated by adopting level's Test statistics, which comprises the following steps:
the Signal data in table 1-1 is converted by Log10 using Log conversion submodule-1, the converted data is LgSignal as shown in table 1-3, and the variance alignment of LgSignal is detected by using level's Test statistical calculation submodule.
Tables 1-3Log10 conversion NC Signal values
/>
The results of variance alignment based on LgSignal data (test results) of tables 1-3 above show that variance alignment p < 0.05 for different test boards and variance alignment p < 0.05 between different analysts, variance is not uniform, so that data needs to be carefully selected or unified calculation cannot be performed or re-calculation is required in the case of manual calculation, work efficiency of researchers is greatly delayed, and no effective solution exists at present. However, according to the invention, different values of the LgSignal variance alignment of different analysis batches are further statistically measured, wherein the LgSignal variance alignment between different analysis batches is p (0.7894) >0.05, which indicates that the variance alignment is p (0.7894) >0.05, which indicates that the variance alignment between different analysis batches is, and the variance alignment indicates that the variance alignment of NC signal values is uniform, so that the step 2) can be performed as effective data.
Step 2) performing Residual value calculation by using the logarithmic conversion sub-module-2 of the individual value preprocessing module, taking a sample "S01" as an example for explanation, obtaining LgSNR data after 4 parallel detection values of S01 are converted by Log10, wherein the LgSNR data are respectively 0.068185862,0.068211112,0.114093135 and 0.092395388, the bit number (MedianLgSNR) is 0.08030325, and the corresponding Residual value Residual is obtained by calculating LgSNR-MedianLgSNR to be-0.012117388, -0.012092138,0.033789885 and 0.012092138.
Step 3) eliminating invalid data by using an outlier eliminating module;
step 3-1) detection of analytical outliers of multiple parallel detection results of individual biological individuals:
the variance alignment test result in the previous step 1) shows that the variances of NC signal values are aligned, experimental data meet the requirements, and outlier detection can be further carried out on sample detection values. The results of the detection of the analysis outliers are shown in Table 3-1 and Table 3-2.
And 4 residual values are subjected to outlier inspection under the alpha of 0.05 level by adopting a Dixon's Test statistical calculation sub-module, if the analysis outlier is detected, the analysis outlier is marked as AO after corresponding data, and the analysis outlier is removed in the subsequent data processing process. In this example, an analytical outlier is detected in the S40 sample.
TABLE 3-1 analysis outlier test output results
/>
/>
/>
/>
/>
/>
/>
/>
TABLE 3-2Dixon's analysis outlier test p-value summary table
/>
/>
Step 3-2) detecting biological outliers of a plurality of parallel detection results of a plurality of biological individuals:
after the outlier analysis is finished and removed, the median (MedianLgSNR_AOR) of the single sample parallel detection value Log10 after conversion is calculated again, and then the k times of the bit distance detection statistical calculation submodule is utilized to detect the biological outlier by adopting the k times of the bit distance pair obtained data, wherein the k value is 1.5. The median results are shown in Table 3-3, and based on the above tables 3-1 and 3-2, the determination that the absolute value of the median (MedianLgSNR_AOR) after detection with k-times the bit distance is greater than 0.1 (MedianLgSNR_AOR < -0.1 and MedianLgSNR_AOR > 0.1) is a biological outlier, in this example S19 and S39 are biological outliers.
TABLE 3 analysis of sample median summary table after outlier removal
/>
And 4) processing the data by using a checking module and outputting a result. The distribution state of the residual data is checked by adopting a shape Wilk's statistical calculation sub-module and a Shewness Test statistical calculation sub-module respectively:
and (3) checking the residual data (residual Value calculated median data) with outliers removed by adopting different statistical methods, wherein a Shapiro Wilk's checking result shows that the W Value is 0.99476, the Pvalue is 0.7438 & gt 0.05, the residual data accords with the normality, a Shewness Test statistical method checking result shows that Shewness value= 0.005253687, the data does not show obvious bias distribution, and the method has good credibility and accuracy. And outputs a graphic result diagram as shown in fig. 2, which performs the same calculation with all data without outliers removed and outputs the graphic result diagram as shown in fig. 3. As is evident from comparing fig. 2 and 3, all data points in the Sample lights-Theorelical Quantiles graph of fig. 3 are in the distribution pattern, with higher accuracy and data reliability.
Step 5) calculating a group of Cut-Points values at a specified 5% false positive rate level
Assuming that the false positive rate is 5%, substituting the distribution mode detection result and/or the graph result obtained in the step 4), and substituting the graph result shown in fig. 2 in this example. The resulting set Points value group includes 1.167,1.104,1.162. Namely, the system function is completed, and a value group formed by possible Cut Points can be obtained quickly and accurately after the original data is input.
The raw data of this example was calculated based on the conventional method described in Devanarayan V, smith W C, brunelle R L, et al Recommendations for Systematic Statistical Computation of Immunogenicity Cut Points [ J ]. Aaps Journal,2017,19 (5.) and the conventional method described in the guidelines for drug immunogenicity research published by the drug administration at day 3 and 5 of 2021.
And (3) displaying a calculation result: for normal distribution data (shape Wilk p > 0.05) after outliers are removed, scpf_lgback=1.168 is the optimal Cut Point selected; for data which shows non-normal distribution (shape Wilk p is less than or equal to 0.05) and non-biased distribution (Shewness is less than or equal to 1) after outliers are removed, SCPF_Rob_LgBack=1.103 is the optimal Cut Point; for data which shows non-normal distribution (shape Wilk p is less than or equal to 0.05) and shows bias distribution (Shewness > 1) after outlier removal, 95per cent=1.162 is the optimal Cut Point.
The final results obtained by the standard routine are basically identical with the results obtained by the method and the system of the invention, and the corresponding distribution mode is the same as the test result in the step 4) of the invention. The method and the system of the invention are proved to have extremely high calculation accuracy.
The conventional method needs about 4-6 hours for calculation by a biological analysis student, errors are prone to occur in the calculation process, the technical scheme of the invention can shorten the calculation time by more than 90%, and based on a system formed by the method, a final Cut Points value group can be obtained within about 10 minutes after the original data are input, and the obtained Cut Points value group can be rapidly judged only by the biological analysis student in combination with the graph result diagram obtained in the step 4), so that the working efficiency of the system is greatly improved.
Claims (7)
1. A system for establishing a threshold population of drug-resistant antibodies is characterized in that,
the system comprises:
the system comprises a signal evaluation module, an individual value preprocessing module, an outlier rejection module, a checking module and a calculation output module;
the signal evaluation module outputs a detection result;
the individual value preprocessing module calculates a data residual value;
the outlier removing module removes and analyzes outliers and biological outliers;
the inspection module outputs a distributed inspection result and/or a graphic result;
the calculation output module calculates a Cut-Points value group according to the specified false positive rate and the distribution mode test result and/or the graph result;
the signal evaluation module is used for receiving the original information, outputting a detection result and detecting the variance alignment, and is embedded with a level's Test statistical calculation sub-module;
the original information comprises a negative signal value and a signal source label;
the signal source tag includes at least one of an analyst and/or an analysis batch;
the signal evaluation module carries out logarithmic transformation on the negative signal value of the received original information to form a detection result and outputs the detection result;
the signal evaluation module is used for checking variance alignment of negative signal values corresponding to different signal source labels on the signal source labels through a level's Test statistical calculation sub-module after the original information is received;
the individual value preprocessing module receives the detection result output by the signal evaluation module and calculates a residual value through logarithmic transformation;
the outlier eliminating module is embedded with a k times bit distance detection statistics computation sub-module and/or a Dixon's Test statistics computation sub-module, eliminates and analyzes an outlier and a biological outlier based on a detection result and/or a residual error value, and then outputs a residual detection result to the detection module;
the detection module is embedded with a shape Wilk's statistical computation sub-module and/or a Shewness Test statistical computation sub-module, and the detection module receives the residual detection result output by the outlier rejection module, and then calculates and outputs a distribution mode detection result and/or a graph result by using the shape Wilk's statistical computation sub-module and/or the Shewness Test statistical computation sub-module;
the calculation output module sets false positive rate and substitutes the false positive rate into a distribution mode detection result and/or a graph result to calculate and output a Cut-Points value group.
2. A method for implementing the system of claim 1, characterized in that,
the method comprises the following steps:
1) Carrying out logarithmic conversion on the negative signal value on the analysis board, and outputting a detection result;
2) According to the detection result of the step 1), carrying out logarithmic transformation on the multiple parallel detection results of the single organism, and calculating residual values of the multiple parallel detection results of the single organism;
3) Detecting an analysis outlier and a biological outlier of a plurality of parallel detection results of a single biological individual based on the detection result of the step 1) and the residual value obtained in the step 2), and removing the analysis outlier and the biological outlier;
4) Adopting a statistical method to test the distribution mode of the residual data after the outlier and the biological outlier are removed and analyzed in the step 3), and outputting a test result of the distribution mode or simultaneously outputting a graph result;
5) Calculating a Cut-Points value group under a specified false positive rate level according to the distribution mode test result and/or the graph result obtained in the step 4);
wherein, step 1) adopts a level's Test statistical method to detect the source of different analysis plates and the variance alignment of different analysis plates.
3. The method of claim 2, wherein the method comprises the steps of establishing a calculated threshold population of anti-drug antibodies,
the log10 log transformation is used for the log transformation in step 1).
4. The method of claim 2, wherein the method comprises the steps of establishing a calculated threshold population of anti-drug antibodies,
the detection in the step 3) is carried out by adopting a k times bit distance detection statistical method and/or a Dixon's Test statistical method.
5. The method of claim 2, wherein the method comprises the steps of establishing a calculated threshold population of anti-drug antibodies,
and 4) checking and outputting a distributed mode checking result by adopting a Shapiro Wilk's statistical method and/or a Shewness Test statistical method.
6. A method of establishing a computational threshold population of anti-drug antibodies according to claim 2 or 5,
the graphical result form of step 4) includes at least one of box plot, Q-Q plot, frequency histogram, and density plot.
7. The method of claim 2, wherein the method comprises the steps of establishing a calculated threshold population of anti-drug antibodies,
step 5) the appointed false positive rate is 1-5%;
the set-Points value group includes at least two set-Points values.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110944366.4A CN113674796B (en) | 2021-08-17 | 2021-08-17 | Method for establishing drug-resistant antibody calculation threshold group and system for realizing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110944366.4A CN113674796B (en) | 2021-08-17 | 2021-08-17 | Method for establishing drug-resistant antibody calculation threshold group and system for realizing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113674796A CN113674796A (en) | 2021-11-19 |
| CN113674796B true CN113674796B (en) | 2024-02-20 |
Family
ID=78543462
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110944366.4A Active CN113674796B (en) | 2021-08-17 | 2021-08-17 | Method for establishing drug-resistant antibody calculation threshold group and system for realizing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113674796B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107564029A (en) * | 2017-07-24 | 2018-01-09 | 南京信息工程大学 | Moving target detecting method based on the filtering of Gauss extreme value and the sparse RPCA of group |
| CN111157698A (en) * | 2019-12-24 | 2020-05-15 | 核工业北京地质研究院 | Inversion method for obtaining total potassium content of black soil by using emissivity data |
| CN112568901A (en) * | 2020-12-09 | 2021-03-30 | 常州大学 | Gait symmetry and consistency evaluation method based on multiple sensors |
| CN112669901A (en) * | 2020-12-31 | 2021-04-16 | 北京优迅医学检验实验室有限公司 | Chromosome copy number variation detection device based on low-depth high-throughput genome sequencing |
| CN113158448A (en) * | 2021-04-07 | 2021-07-23 | 大连海事大学 | Self-adaptive calculation method for equipment threshold of ship system |
| CN113188981A (en) * | 2021-04-30 | 2021-07-30 | 天津深析智能科技发展有限公司 | Automatic analysis method of multi-factor cytokine |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040138826A1 (en) * | 2002-09-06 | 2004-07-15 | Carter Walter Hansbrough | Experimental design and data analytical methods for detecting and characterizing interactions and interaction thresholds on fixed ratio rays of polychemical mixtures and subsets thereof |
-
2021
- 2021-08-17 CN CN202110944366.4A patent/CN113674796B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107564029A (en) * | 2017-07-24 | 2018-01-09 | 南京信息工程大学 | Moving target detecting method based on the filtering of Gauss extreme value and the sparse RPCA of group |
| CN111157698A (en) * | 2019-12-24 | 2020-05-15 | 核工业北京地质研究院 | Inversion method for obtaining total potassium content of black soil by using emissivity data |
| CN112568901A (en) * | 2020-12-09 | 2021-03-30 | 常州大学 | Gait symmetry and consistency evaluation method based on multiple sensors |
| CN112669901A (en) * | 2020-12-31 | 2021-04-16 | 北京优迅医学检验实验室有限公司 | Chromosome copy number variation detection device based on low-depth high-throughput genome sequencing |
| CN113158448A (en) * | 2021-04-07 | 2021-07-23 | 大连海事大学 | Self-adaptive calculation method for equipment threshold of ship system |
| CN113188981A (en) * | 2021-04-30 | 2021-07-30 | 天津深析智能科技发展有限公司 | Automatic analysis method of multi-factor cytokine |
Non-Patent Citations (2)
| Title |
|---|
| 一种适宜于子空间聚类的离群点检测算法;杨维永;何军;郑生军;张旭东;;计算机与现代化(第12期);全文 * |
| 基于多引导滤波的图像增强算法;刘杰;张建勋;代煜;;物理学报(第23期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113674796A (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107862338B (en) | Marine environment monitoring data quality management method and system based on double inspection method | |
| US6556951B1 (en) | System and method for intelligent quality control of a process | |
| CN110660462A (en) | Inspection report automatic auditing method, system and storage medium based on big data | |
| CN112365927B (en) | CNV detection device | |
| CN115691722B (en) | Quality control method, device, equipment, medium and program product for medical data detection | |
| EP4310701A1 (en) | Homogenization conversion method and apparatus for detection of same index by different devices, and electronic device | |
| CN116612820B (en) | Dairy product production intelligent management platform based on data analysis | |
| CN111400505A (en) | Method and system for matching fault elimination scheme of power consumption information acquisition system | |
| CN116150861B (en) | Intelligent processing system for construction data of shear wall in high-intensity area | |
| CN111833009A (en) | Intelligent auditing software system for whole laboratory | |
| CN113674796B (en) | Method for establishing drug-resistant antibody calculation threshold group and system for realizing method | |
| CN112102903B (en) | Quality control system based on clinical laboratory testing result | |
| WO2022134579A1 (en) | Method and apparatus for detecting abnormalities of service transaction data, and computer device | |
| CN114944208B (en) | Quality control method, quality control device, electronic equipment and storage medium | |
| CN108536777B (en) | Data processing method, server cluster and data processing device | |
| CN114496196A (en) | Automatic auditing system for clinical biochemical inspection in medical laboratory | |
| CN113447455B (en) | Fiber material detection system based on terahertz technology | |
| US6766265B2 (en) | Processing tester information by trellising in integrated circuit technology development | |
| US20160265051A1 (en) | Methods for Detection of Fetal Chromosomal Abnormality Using High Throughput Sequencing | |
| Sari | The Effectiveness of Statistics Software Training to Improve the Understanding of Quantitative Research Methodology Concepts for Students of Tadris Mathematics, Faculty of Tarbiyah and Teacher Training, UIN Maulana Malik Ibrahim Malang | |
| CN114389840B (en) | Method and system for determining area where network attack source is located based on GLM factorization method | |
| CN112765011B (en) | Quality control state judging method and device and electronic equipment | |
| CN114580982B (en) | Method, device and equipment for evaluating data quality of industrial equipment | |
| CN112162102B (en) | Correction method for four blood coagulation tests | |
| CN118298918B (en) | Mitochondrial gene proportion quality control and threshold determination method, system and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |