CN116884478A - Proteomics data analysis method, device, electronic equipment and storage medium - Google Patents
Proteomics data analysis method, device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116884478A CN116884478A CN202310744096.1A CN202310744096A CN116884478A CN 116884478 A CN116884478 A CN 116884478A CN 202310744096 A CN202310744096 A CN 202310744096A CN 116884478 A CN116884478 A CN 116884478A
- Authority
- CN
- China
- Prior art keywords
- analysis
- protein expression
- protein
- differential
- carrying
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000007405 data analysis Methods 0.000 title claims abstract description 44
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 393
- 102000004169 proteins and genes Human genes 0.000 claims abstract description 371
- 230000014509 gene expression Effects 0.000 claims abstract description 217
- 230000008859 change Effects 0.000 claims abstract description 88
- 238000004458 analytical method Methods 0.000 claims abstract description 73
- 238000010201 enrichment analysis Methods 0.000 claims abstract description 62
- 238000012360 testing method Methods 0.000 claims abstract description 25
- 238000010219 correlation analysis Methods 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims description 51
- 238000010586 diagram Methods 0.000 claims description 31
- 238000003908 quality control method Methods 0.000 claims description 27
- 238000000513 principal component analysis Methods 0.000 claims description 15
- 238000005516 engineering process Methods 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 13
- 230000037361 pathway Effects 0.000 claims description 11
- 230000003993 interaction Effects 0.000 claims description 10
- 238000012512 characterization method Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 238000000692 Student's t-test Methods 0.000 claims description 6
- 238000001962 electrophoresis Methods 0.000 claims description 6
- 238000012353 t test Methods 0.000 claims description 6
- 101000812677 Homo sapiens Nucleotide pyrophosphatase Proteins 0.000 claims description 5
- 101100353526 Neurospora crassa (strain ATCC 24698 / 74-OR23-1A / CBS 708.71 / DSM 1257 / FGSC 987) pca-2 gene Proteins 0.000 claims description 5
- 102100039306 Nucleotide pyrophosphatase Human genes 0.000 claims description 5
- 238000012795 verification Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 4
- 238000010199 gene set enrichment analysis Methods 0.000 claims description 3
- 238000004949 mass spectrometry Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 11
- 230000000694 effects Effects 0.000 description 8
- 239000004973 liquid crystal related substance Substances 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 230000008827 biological function Effects 0.000 description 3
- 230000031018 biological processes and functions Effects 0.000 description 3
- 239000003086 colorant Substances 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000006854 communication Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000006916 protein interaction Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000000491 multivariate analysis Methods 0.000 description 2
- 238000010202 multivariate logistic regression analysis Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002093 peripheral effect Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000000018 DNA microarray Methods 0.000 description 1
- 206010061818 Disease progression Diseases 0.000 description 1
- 238000003559 RNA-seq method Methods 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008236 biological pathway Effects 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000004186 co-expression Effects 0.000 description 1
- 230000008045 co-localization Effects 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013079 data visualisation Methods 0.000 description 1
- 230000005750 disease progression Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 239000003596 drug target Substances 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 150000002632 lipids Chemical class 0.000 description 1
- 229920002521 macromolecule Polymers 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000001819 mass spectrum Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000010399 physical interaction Effects 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 239000012474 protein marker Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012800 visualization Methods 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
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- 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
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- 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
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/30—Unsupervised data analysis
-
- 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
- G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
-
- 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
- G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
- G16B50/10—Ontologies; Annotations
Abstract
The invention relates to the field of data processing, and discloses a proteomics data analysis method, a proteomics data analysis device, electronic equipment and a storage medium, wherein the proteomics data analysis method comprises the following steps: acquiring protein chip data of a clean test sample; performing differential analysis on the protein chip data to obtain a first differential protein expression set; carrying out differential protein recognition of a mixed linear model on protein chip data according to a preset sample multivariable factor to obtain a second differential protein expression set, and forming a total differential protein expression set with the first differential protein expression set; carrying out first enrichment analysis on the total differential protein expression set to obtain a differential protein expression change set; carrying out second enrichment analysis on the protein chip data to obtain a whole protein expression change set; and carrying out correlation analysis together with the differential protein expression change set to obtain a multivariate protein analysis result. The invention is mainly aimed at increasing the comprehensiveness and accuracy of proteomics data analysis.
Description
Technical Field
The invention relates to a proteomics data analysis method, a proteomics data analysis device, electronic equipment and a storage medium, and belongs to the field of data processing.
Background
Proteins are well known as macromolecules which ultimately perform biological functions in humans, and whose expression levels are more closely related to disease, lifestyle, phenotype, and allow for more real-time monitoring of disease progression.
Traditional proteomics, protein marker detection methods have various limitations in terms of throughput, sensitivity, and ability to clinically transform. Proteomics techniques with excellent properties of high throughput, low volume, targeting, etc. are thus increasingly moving towards maturity. However, except that the existing OlinkAnalyze R package and the extension packages developed by some others in the Olink platform can perform some basic analysis, a clear mining and analyzing method for a multivariable sample is not available, and the multivariable comprehensive correlation analysis of a protein sample cannot be satisfied.
Disclosure of Invention
The invention provides a proteomics data analysis method, a proteomics data analysis device, an electronic device and a storage medium, and mainly aims to obtain a comprehensive and accurate protein analysis result by performing multi-test comprehensive analysis on proteomics data.
In order to achieve the above object, the present invention provides a proteomic data analysis method, comprising:
removing unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
performing differential analysis on the protein chip data to obtain a first differential protein expression set;
carrying out differential protein recognition on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set;
carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
and performing correlation analysis on the differential protein expression change set and the whole protein expression change set to obtain a multivariate protein analysis result.
Optionally, the performing differential analysis on the protein chip data to obtain a first differential protein expression set includes:
carrying out principal component analysis operation based on the conditions of PCA1 and PCA2 on the protein chip data by utilizing OlinkAnalyze to obtain a principal component analysis chart;
judging whether the principal component analysis diagram accords with normal distribution;
when the principal component analysis chart accords with normal distribution, performing t-test operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a test result;
and when the principal component analysis chart does not accord with normal distribution, carrying out wilcox detection operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a detection result.
Optionally, the enrichment analysis of the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set includes:
configuring C2 and C5 data sets of the pre-constructed Msigdbr as reference parameters, and configuring pre-constructed GO and KEGG database information as path parameters;
and carrying out enrichment analysis on the total differential protein expression set according to the reference parameters and the channel parameters by using the gene set enrichment analysis method in OlinkAnalyze to obtain a differential protein expression change set.
Optionally, the enriching and analyzing the protein chip data according to a preset second enriching and analyzing strategy to obtain a total protein expression change set, including:
acquiring gene annotation information in a pre-constructed ClusterProfiler packet;
and carrying out pretreatment operation based on cleaning, normalization and standardization on the protein chip data, and carrying out enrichment analysis operation on pretreatment results according to the gene annotation information to obtain a whole protein expression change set.
Optionally, removing unqualified proteins in the target detection sample according to a preset protein quality control strategy to obtain a clean detection sample, including:
separating proteins from a target detection sample by using an electrophoresis method, and identifying the separated proteins by using a mass spectrometry technology to obtain a protein analysis result of each separation area;
and filtering the high-representation, unmodified and polluted proteins in the protein analysis result according to a preset protein quality control strategy to obtain a net test sample.
Optionally, the performing correlation analysis on the differential protein expression change set and the total protein expression change set to obtain a multivariate protein analysis result includes:
Extracting the NPX expression matrix after the differential protein expression change set and the total protein expression change set are combined;
and performing correlation analysis on the NPX expression matrix to obtain a multivariate protein analysis result comprising the Szellman correlation coefficient, the correlation coefficient matrix and the Euclidean distance.
Optionally, after the multivariate protein analysis result is obtained, the method further comprises:
constructing a quality control chart and a quarter-division chart according to the protein chip data by utilizing the OlinkAnalyze;
constructing volcanic and thermal maps according to the first differential protein expression set;
constructing an inter-group expression diagram and an expression box diagram according to the second differential protein expression set;
constructing a pathway enrichment bar graph and a bubble graph according to the differential protein expression change set;
constructing an enrichment pathway network diagram and a Gseapalot diagram according to the whole protein expression change set;
the differential protein expression change set and the whole protein expression change set are imported into a pre-constructed STRING database to obtain a multi-variable protein network interaction map;
and storing the quality control map and the quartering map, the volcanic map and the heat map, the inter-group expression map and the expression box map, the enrichment pathway network map and the Gseapalot map, and the multivariable protein network interaction map into a pre-constructed post-verification database.
In order to solve the above problems, the present invention also provides a proteomic data analysis device including:
the sample quality control analysis module is used for removing unqualified proteins in the target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
the differential analysis module is used for carrying out differential analysis on the protein chip data to obtain a first differential protein expression set, carrying out differential protein identification on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
the enrichment analysis module is used for carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set, and carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
And the correlation analysis module is used for carrying out correlation analysis on the differential protein expression change set and the total protein expression change set to obtain a multivariate protein analysis result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to implement the proteomic data analysis method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one instruction that is executed by a processor in an electronic device to implement the proteomic data analysis method described above.
Compared with the problems in the background art, the embodiment of the invention sequentially analyzes and obtains the first differential protein expression set, the second differential protein expression set, the differential protein expression change set, the total protein expression change set and the multivariate protein analysis result, continuously integrates the analysis results of a plurality of tests, increases the number of candidate proteins, expands the search range and realizes the analysis mode of a plurality of tests; when the second differential protein expression set is obtained, the second differential protein expression set is realized through a mixed linear model, and the mixed linear model can be well adapted to a multivariate analysis task; the invention can meet the requirement of the Olink platform on the comprehensive analysis of the protein by a multi-test and multi-variable analysis mode; in addition, the first differential protein expression set, the second differential protein expression set, the differential protein expression change set, the total protein expression change set and the multivariate protein analysis result can be visually stored, so that when post-detection is conveniently carried out, the difference information can be accurately obtained between the two groups of detection data. Therefore, the proteomics data analysis method, the proteomics data analysis device, the electronic equipment and the computer readable storage medium mainly aim to obtain a more comprehensive and accurate protein analysis result by performing multi-test comprehensive analysis on the proteomics data.
Drawings
FIG. 1 is a flow chart of a method for proteomics data analysis according to an embodiment of the present application;
FIG. 2 is a functional block diagram of a proteomics data analysis device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device for implementing the proteomic data analysis method according to an embodiment of the application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a proteomics data analysis method. The main implementation body of the proteomics data analysis method includes, but is not limited to, at least one of a server, a terminal and the like which can be configured to implement the method provided by the embodiment of the application. In other words, the proteomics data analysis method may be performed by software or hardware installed at a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
referring to fig. 1, a flow chart of a proteomics data analysis method according to an embodiment of the invention is shown. In this embodiment, the proteomic data analysis method includes:
s1, removing unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data.
In the embodiment of the invention, the protein quality control strategy is a method for filtering proteins or other substances influencing the detection process of the protein to be detected according to the expression characteristics of the protein to be detected.
In detail, in the embodiment of the present invention, according to a preset protein quality control policy, removing unqualified proteins in a target detection sample to obtain a net detection sample, including:
separating proteins from a target detection sample by using an electrophoresis method, and identifying the separated proteins by using a mass spectrometry technology to obtain a protein analysis result of each separation area;
and filtering the high-representation, unmodified and polluted proteins in the protein analysis result according to a preset protein quality control strategy to obtain a net test sample.
The electrophoresis method is a method for separating proteins in a protein sample according to characteristics such as charge, size, morphology and the like.
Specifically, when the electrophoresis method is implemented on the target detection sample, unmodified proteins with higher molecular weight and charged amount, high-expression proteins which interfere with detection of other proteins due to over-high expression brightness, and high-concentration pollutants such as colloid, lipid and the like are found in the electrophoresis process, and according to the protein quality control strategy, the substances belong to unqualified proteins for the proteins to be detected and are required to be filtered, so that a clean detection sample is obtained.
Furthermore, the embodiment of the invention utilizes the protein chip technology to characterize and identify the net test sample so as to obtain protein chip data. The protein chip technology is a high-throughput biochip technology, and is commonly used in the fields of detecting protein interaction, identifying downstream signal paths of proteins, screening drug targets and the like, and specific processing procedures thereof are not described herein.
In addition, in the embodiment of the invention, the protein chip data can be subjected to visual expansion to construct a quality control diagram and a quarter-bit diagram. The quality control chart can be used for evaluating the quality of protein chip data, generally displaying the difference between samples and the distribution condition of the data, and can help to detect whether the data has abnormal values, batch effects, experimental errors and other problems; the quartile is a data analysis graph, and can sort data according to size and divide it into four equal parts, namely a minimum value, a lower quartile, a median, an upper quartile and a maximum value. The distribution condition of the data can be intuitively known through the quartering bitmap, and the data distribution condition comprises information such as central trend, discrete degree, abnormal value and the like of the data, so that data analysis and statistical analysis are further carried out. In the embodiment of the invention, the quality control diagram and the quartering diagram are visualized, and the inter-group expression diagram, the expression box diagram and other diagrams obtained by various detection methods described below are all stored in a pre-constructed post verification database, so that various detection results can be checked in return when post verification is convenient, and the two groups of variables between which each group of difference information appears can be accurately obtained.
S2, performing differential analysis on the protein chip data to obtain a first differential protein expression set.
Wherein, the OlinkAnalyze is a style of data analysis and visualization tool for analyzing protein chip data of an Olink platform.
In detail, in the embodiment of the present invention, performing differential analysis on the protein chip data to obtain a first differential protein expression set includes:
carrying out principal component analysis operation based on the conditions of PCA1 and PCA2 on the protein chip data by utilizing OlinkAnalyze to obtain a principal component analysis chart;
judging whether the principal component analysis diagram accords with normal distribution;
when the principal component analysis chart accords with normal distribution, performing t-test operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a test result;
and when the principal component analysis chart does not accord with normal distribution, carrying out wilcox detection operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a detection result.
It should be appreciated that the t-test is a method that can be used to compare whether there is a significant difference in mean between two samples, and the wilcox test is a non-parametric test method that can be used to compare whether there is a significant difference in median between two samples.
In the protein chip data analysis of the embodiment of the invention, the t-test can be used for comparing whether the expression levels of different proteins in two groups of samples have obvious differences or not; the wilcox test can be used to compare whether there is a significant difference in the expression levels of different proteins in the two sets of samples. However, the premise of the t-test is that the sample data belongs to normal distribution, while the wilcox test has wider application range, and is not limited to the normal distribution of the sample data. In the embodiment of the invention, the main component analysis can be utilized to analyze and visualize the dimension reduction of the protein chip data, so that whether the sample is normally distributed or not can be checked conveniently.
Specifically, the invention can reduce the dimension of the protein chip data into two-dimensional data by using the OlinkAnalyze to draw PCA1 and PCA2, and visualize the two-dimensional data in a plane coordinate system. The differences and the similarities among the samples and the distribution condition of the data can be intuitively known through the PCA1 and PCA2 condition diagrams. And after the data distribution is determined, selecting a proper detection method to obtain a first differential protein expression set.
In addition, the embodiment of the invention can also utilize OlinkAnalyze to visualize the first differential protein expression set, obtain volcanic images and heat images and store the volcanic images and heat images in the post-verification database. Wherein, the volcanic image generally displays the difference level and the significance of different proteins in a graph, and the quantity and the degree of the different proteins can be intuitively understood. The heat map can map the expression levels of all proteins in all samples into a matrix, and different colors are used for representing different expression levels, so that the similarity and the difference between the samples can be found.
S3, utilizing a pre-constructed mixed linear model, carrying out differential protein recognition on the protein chip data according to a preset sample multivariable factor to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set.
The mixed linear model is a model for analyzing autonomous variation multivariable in OlinkAnalyze, a multivariable factor of samples can be used as a fixed effect, and random variation among samples can be used as a random effect, so that the influence of differences among samples on a difference analysis result is reduced. Where the fixed effect generally refers to a multivariate factor of the samples, such as age, sex, disease state, etc., and the random effect refers to random variation between samples.
The multivariate factors of the target test samples in the embodiments of the present invention are important because these factors may have an effect on the protein expression levels, thereby interfering with the results of the differential analysis. According to the embodiment of the invention, the automatic analysis of the multivariate data is realized through the mixed linear model, and the second differential protein expression set is obtained.
Furthermore, in order to continuously integrate analysis results of multiple tests, the embodiment of the invention increases the number of candidate proteins, enlarges the search range, realizes a multi-test analysis mode, and combines the second differential protein expression set and the second differential protein expression set to obtain a total differential protein expression set.
In addition, the embodiment of the invention can also visualize the second differential protein expression set, and the OlinkAnalyze is used for drawing images to display the expression quantity and the whole expression quantity of the proteins among different groups, so as to obtain an inter-group expression diagram and an expression box diagram, thereby retaining the visual evidence of the inspection.
S4, carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set.
In the embodiment of the invention, the first enrichment analysis strategy refers to the analysis strategy of C2 and C5 data sets of reference Msigdbr, and the path contains and is less than GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) database information. Wherein the C2 and C5 datasets comprise a plurality of known biological pathway information. The GO and KEGG database information is used as reference information of the pathway, so that the biological function and action mechanism of the pathway can be better understood.
In detail, in the embodiment of the present invention, the enrichment analysis is performed on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set, including:
configuring C2 and C5 data sets of the pre-constructed Msigdbr as reference parameters, and configuring pre-constructed GO and KEGG database information as path parameters;
and carrying out enrichment analysis on the total differential protein expression set according to the reference parameters and the channel parameters by using the gene set enrichment analysis method in OlinkAnalyze to obtain a differential protein expression change set.
According to the first enrichment analysis strategy, the invention can analyze the expression change and the functional characteristics of the gene set of the target detection sample under different biological conditions, so as to obtain a differential protein expression change set. Wherein the set of differential protein expression changes can also be used to calculate a gene ratio value, geneRatio, typically by comparing the base factors in the enrichment analysis results with the total number of genes in the pathway to calculate the gene ratio value for the enrichment analysis. The gene ratio value can help us to know the expression change and biological function of the channel in the sample and the expression condition of the genes in the channel in the sample.
In addition, the analysis method for enriching the gene set in the OlinkAnalyze can still perform data visual expansion to obtain a channel enrichment bar graph and a bubble graph. The channel enrichment bar graph can display enrichment results of different channels in a bar form, the height of the bar represents enrichment score or enrichment proportion of the channels, and the bar with different colors can represent different channel classifications or channel types; the bubble diagram can display the enrichment results of different channels in the form of bubbles, the size of the bubbles represents the enrichment score or enrichment proportion of the channels, and the bubbles with different colors can represent different channel classifications or channel types.
S5, carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a whole protein expression change set.
In the embodiment of the invention, the second enrichment analysis strategy refers to a method for analyzing all proteins by using GSEA of ClusterProfiler. Wherein the clusterifier is an R language package for functional enrichment and visualization of bioinformatics data, mainly for high throughput data analysis in biology, such as RNA-seq, proteomics.
In detail, in the embodiment of the present invention, the enrichment analysis is performed on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set, including:
acquiring gene annotation information in a pre-constructed ClusterProfiler packet;
and carrying out pretreatment operation based on cleaning, normalization and standardization on the protein chip data, and carrying out enrichment analysis operation on pretreatment results according to the gene annotation information to obtain a whole protein expression change set.
Wherein the gene annotation information may be a gene name, a gene ID, pathway information, etc.; and the protein chip data are subjected to the steps of data cleaning, normalization, standardization and the like, so that the consistency and comparability of the data can be ensured. In the ClusterProfiler, the GseGO function can be used for enrichment analysis based on the gene set, and the function can utilize annotation information in the GO database to carry out enrichment analysis on the gene set so as to obtain a whole protein expression change set.
In addition, the embodiment of the invention visualizes the enrichment analysis result of the ClusterProfiler by using Cnetplot and Gseaplot2 functions to obtain an enrichment passage network diagram and a Gseaplot diagram. The Cnetplot function can display the channel enrichment result in the form of a network diagram to obtain an enrichment channel network diagram, wherein nodes represent channels, the size and the color of the nodes represent information such as enrichment scores, p values and the like of the channels, and edges represent the relation among the channels; while the gsearlot 2 function may show the results of GSEA analysis in the form of a line graph, where the horizontal axis represents ordering of gene sets, the vertical axis represents cumulative enrichment score, and the different colored lines represent different pathways or gene sets.
S6, carrying out correlation analysis on the differential protein expression change set and the whole protein expression change set to obtain a multivariate protein analysis result.
The differential protein expression change set and the whole protein expression change set obtained by the embodiment of the invention can be used for further analysis, such as calculating the expression quantity of the differential protein expression change set and the whole protein expression change set in a sample and calculating the correlation between the differential protein expression change set and the whole protein expression change set. These assays can help us to better understand the relationships between proteins and their changes in expression in different samples, revealing their role in biological processes.
In detail, in the embodiment of the present invention, performing correlation analysis on the differential protein expression change set and the total protein expression change set to obtain a multivariate protein analysis result, where the method includes:
extracting the NPX expression matrix after the differential protein expression change set and the total protein expression change set are combined;
and performing correlation analysis on the NPX expression matrix to obtain a multivariate protein analysis result comprising the Szellman correlation coefficient, the correlation coefficient matrix and the Euclidean distance.
Wherein the NPX value in the NPX expression matrix is an index for measuring the protein expression level, which is calculated by converting the mass spectrum signal intensity of the protein into a normalized number. The higher the NPX value, the higher the expression level of the protein.
Further, the spearman correlation coefficient is a non-parametric statistical method for measuring the correlation between two variables, the calculation of which is based on the difference between the two variable levels. For example, if the spearman correlation coefficient of two proteins is high, it is indicated that there may be a functional or regulatory relationship between them. And a matrix of correlation coefficients can be used to describe the correlation between the various proteins.
Further, euclidean distance can be used to measure the difference between them, which is the most common index of correlation analysis.
According to the embodiment of the invention, through correlation analysis, a multivariate protein analysis result comprising the spearman correlation coefficient, the correlation coefficient matrix and the Euclidean distance is obtained, so that the interaction relation among proteins and the expression change of the proteins in different samples can be better known, and the effect of the proteins in a biological process is revealed.
In addition, in the embodiment of the invention, the collection of the differential protein expression change collection and the collection of the total protein expression change collection can be imported into a pre-constructed STRING database to obtain a multi-variable protein network interaction map. The protein interaction information collection in the STRING database comprises physical interaction, co-expression, co-localization, co-regulation and other interactions. The invention can obtain the interaction relation between the proteins by mapping the collection of the differential protein expression change collection and the collection of the total protein expression change collection into the protein interaction network in the STRING database, and display the interaction relation in the form of a network diagram; the multivariable protein network interaction map can carry out a series of downstream tasks such as biomarker identification, treatment target identification, drug analysis, gene expression regulation research and the like, and realize the full-scale analysis of proteomics data.
Compared with the problems in the background art, the embodiment of the invention sequentially analyzes and obtains the first differential protein expression set, the second differential protein expression set, the differential protein expression change set, the total protein expression change set and the multivariate protein analysis result, continuously integrates the analysis results of a plurality of tests, increases the number of candidate proteins, expands the search range and realizes the analysis mode of a plurality of tests; when the second differential protein expression set is obtained, the second differential protein expression set is realized through a mixed linear model, and the mixed linear model can be well adapted to a multivariate analysis task; the invention can meet the requirement of the Olink platform on the comprehensive analysis of the protein by a multi-test and multi-variable analysis mode; in addition, the first differential protein expression set, the second differential protein expression set, the differential protein expression change set, the total protein expression change set and the multivariate protein analysis result can be visually stored, so that when post-detection is conveniently carried out, the difference information can be accurately obtained between the two groups of detection data. Therefore, the proteomics data analysis method, the proteomics data analysis device, the electronic equipment and the computer readable storage medium mainly aim to obtain a more comprehensive and accurate protein analysis result by performing multi-test comprehensive analysis on the proteomics data.
Example 2:
FIG. 2 is a functional block diagram of a proteomics data analysis device according to an embodiment of the present invention.
The proteomics data analysis device 100 according to the present invention may be installed in an electronic device. Depending on the functions implemented, the proteomic data analysis device 100 may include a sample quality control analysis module 101, a difference analysis module 102, an enrichment analysis module 103, and a correlation analysis module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules are as follows:
the sample quality control analysis module 101 is configured to remove unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and perform characterization and identification on the net detection sample by using a protein chip technology to obtain protein chip data;
the differential analysis module 102 is configured to perform differential analysis on the protein chip data to obtain a first differential protein expression set, perform differential protein recognition on the protein chip data according to a preset sample multivariate factor by using a preset mixed linear model to obtain a second differential protein expression set, and form a total differential protein expression set from the first differential protein expression set and the second differential protein expression set;
The enrichment analysis module 103 is configured to perform enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set, and perform enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
the correlation analysis module 104 is configured to perform correlation analysis on the differential protein expression change set and the total protein expression change set to obtain a multivariate protein analysis result.
In detail, the modules in the proteomics data analysis device 100 in the embodiment of the present invention use the same technical means as the proteomics data analysis method described in fig. 1 and can produce the same technical effects, and are not described herein.
Example 3:
fig. 3 is a schematic structural diagram of an electronic device for implementing a proteomic data analysis method according to an embodiment of the invention.
The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program, such as a proteomic data analysis program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of proteomic data analysis programs, etc., but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., proteomic data analysis programs, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
Although not shown, for example, the electronic device 1 may also include a power source (such as a battery) for powering the various components,
preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The proteomic data analysis program stored in the memory 11 of the electronic device 1 is a combination of instructions which, when executed in the processor 10, can implement:
Removing unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
performing differential analysis on the protein chip data to obtain a first differential protein expression set;
carrying out differential protein recognition on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set;
carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
and performing correlation analysis on the differential protein expression change set and the whole protein expression change set to obtain a multivariate protein analysis result.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
removing unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
performing differential analysis on the protein chip data to obtain a first differential protein expression set;
carrying out differential protein recognition on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
Carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set;
carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
and performing correlation analysis on the differential protein expression change set and the whole protein expression change set to obtain a multivariate protein analysis result.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of proteomic data analysis, the method comprising:
removing unqualified proteins in a target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
performing differential analysis on the protein chip data to obtain a first differential protein expression set;
carrying out differential protein recognition on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
Carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set;
carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
and performing correlation analysis on the differential protein expression change set and the whole protein expression change set to obtain a multivariate protein analysis result.
2. The method of claim 1, wherein performing a differential analysis on the protein chip data to obtain a first set of differential protein expressions comprises:
carrying out principal component analysis operation based on the conditions of PCA1 and PCA2 on the protein chip data by utilizing OlinkAnalyze to obtain a principal component analysis chart;
judging whether the principal component analysis diagram accords with normal distribution;
when the principal component analysis chart accords with normal distribution, performing t-test operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a test result;
and when the principal component analysis chart does not accord with normal distribution, carrying out wilcox detection operation in the OlinkAnalyze on the protein chip data, and extracting to obtain a first differential protein expression set according to a detection result.
3. The method of claim 1, wherein performing enrichment analysis on the total differential protein expression set according to a predetermined first enrichment analysis strategy to obtain a differential protein expression change set comprises:
configuring C2 and C5 data sets of the pre-constructed Msigdbr as reference parameters, and configuring pre-constructed GO and KEGG database information as path parameters;
and carrying out enrichment analysis on the total differential protein expression set according to the reference parameters and the channel parameters by using the gene set enrichment analysis method in OlinkAnalyze to obtain a differential protein expression change set.
4. The method of claim 1, wherein the performing enrichment analysis on the protein chip data according to a second predetermined enrichment analysis strategy to obtain a total protein expression change set comprises:
acquiring gene annotation information in a pre-constructed ClusterProfiler packet;
and carrying out pretreatment operation based on cleaning, normalization and standardization on the protein chip data, and carrying out enrichment analysis operation on pretreatment results according to the gene annotation information to obtain a whole protein expression change set.
5. The method of claim 1, wherein the removing unacceptable protein from the target test sample according to a predetermined protein quality control strategy to obtain a net test sample comprises:
separating proteins from a target detection sample by using an electrophoresis method, and identifying the separated proteins by using a mass spectrometry technology to obtain a protein analysis result of each separation area;
and filtering the high-representation, unmodified and polluted proteins in the protein analysis result according to a preset protein quality control strategy to obtain a net test sample.
6. The method of claim 1, wherein performing a correlation analysis on the set of differential protein expression changes and the set of total protein expression changes to obtain a multivariate protein analysis result comprises:
extracting the NPX expression matrix after the differential protein expression change set and the total protein expression change set are combined;
and performing correlation analysis on the NPX expression matrix to obtain a multivariate protein analysis result comprising the Szellman correlation coefficient, the correlation coefficient matrix and the Euclidean distance.
7. The method of proteomic data analysis according to any one of claims 1 to 6, wherein after the result of the multivariate protein analysis is obtained, the method further comprises:
constructing a quality control chart and a quarter-division chart according to the protein chip data by utilizing the OlinkAnalyze;
constructing volcanic and thermal maps according to the first differential protein expression set;
constructing an inter-group expression diagram and an expression box diagram according to the second differential protein expression set;
constructing a pathway enrichment bar graph and a bubble graph according to the differential protein expression change set;
constructing an enrichment pathway network diagram and a Gseapalot diagram according to the whole protein expression change set;
the differential protein expression change set and the whole protein expression change set are imported into a pre-constructed STRING database to obtain a multi-variable protein network interaction map;
and storing the quality control map and the quartering map, the volcanic map and the heat map, the inter-group expression map and the expression box map, the enrichment pathway network map and the Gseapalot map, and the multivariable protein network interaction map into a pre-constructed post-verification database.
8. A proteomic data analysis device, the device comprising:
The sample quality control analysis module is used for removing unqualified proteins in the target detection sample according to a preset protein quality control strategy to obtain a net detection sample, and carrying out characterization and identification on the net detection sample by utilizing a protein chip technology to obtain protein chip data;
the differential analysis module is used for carrying out differential analysis on the protein chip data to obtain a first differential protein expression set, carrying out differential protein identification on the protein chip data according to a preset sample multivariable factor by utilizing a preset mixed linear model to obtain a second differential protein expression set, and forming a total differential protein expression set by the first differential protein expression set and the second differential protein expression set;
the enrichment analysis module is used for carrying out enrichment analysis on the total differential protein expression set according to a preset first enrichment analysis strategy to obtain a differential protein expression change set, and carrying out enrichment analysis on the protein chip data according to a preset second enrichment analysis strategy to obtain a total protein expression change set;
and the correlation analysis module is used for carrying out correlation analysis on the differential protein expression change set and the total protein expression change set to obtain a multivariate protein analysis result.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the proteomic data analysis method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the proteomic data analysis method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310744096.1A CN116884478B (en) | 2023-06-20 | 2023-06-20 | Proteomics data analysis method, device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310744096.1A CN116884478B (en) | 2023-06-20 | 2023-06-20 | Proteomics data analysis method, device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116884478A true CN116884478A (en) | 2023-10-13 |
| CN116884478B CN116884478B (en) | 2024-01-05 |
Family
ID=88265259
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310744096.1A Active CN116884478B (en) | 2023-06-20 | 2023-06-20 | Proteomics data analysis method, device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116884478B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130023574A1 (en) * | 2010-03-31 | 2013-01-24 | National University Corporation Kumamoto University | Method for generating data set for integrated proteomics, integrated proteomics method using data set for integrated proteomics that is generated by the generation method, and method for identifying causative substance using same |
| CN109033748A (en) * | 2018-08-14 | 2018-12-18 | 齐齐哈尔大学 | A kind of miRNA identification of function method based on multiple groups |
| US20190106732A1 (en) * | 2017-10-05 | 2019-04-11 | Iquity, Inc. | Long non-coding rna gene expression signatures in disease diagnosis |
| CN114882942A (en) * | 2022-04-01 | 2022-08-09 | 苏州大学 | Quantitative proteomics analysis method for FLASH irradiated tissue |
| CN115472218A (en) * | 2022-09-14 | 2022-12-13 | 杭州链康医学检验实验室有限公司 | Analysis method of Olink proteomics data |
| CN115662517A (en) * | 2022-11-09 | 2023-01-31 | 抖音视界有限公司 | Sequence prediction method, device, medium, and electronic apparatus |
-
2023
- 2023-06-20 CN CN202310744096.1A patent/CN116884478B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130023574A1 (en) * | 2010-03-31 | 2013-01-24 | National University Corporation Kumamoto University | Method for generating data set for integrated proteomics, integrated proteomics method using data set for integrated proteomics that is generated by the generation method, and method for identifying causative substance using same |
| US20190106732A1 (en) * | 2017-10-05 | 2019-04-11 | Iquity, Inc. | Long non-coding rna gene expression signatures in disease diagnosis |
| CN109033748A (en) * | 2018-08-14 | 2018-12-18 | 齐齐哈尔大学 | A kind of miRNA identification of function method based on multiple groups |
| CN114882942A (en) * | 2022-04-01 | 2022-08-09 | 苏州大学 | Quantitative proteomics analysis method for FLASH irradiated tissue |
| CN115472218A (en) * | 2022-09-14 | 2022-12-13 | 杭州链康医学检验实验室有限公司 | Analysis method of Olink proteomics data |
| CN115662517A (en) * | 2022-11-09 | 2023-01-31 | 抖音视界有限公司 | Sequence prediction method, device, medium, and electronic apparatus |
Non-Patent Citations (1)
| Title |
|---|
| 杨睿 等: "基于iTRAQ-PRM技术筛选不同类型土壤烤烟根系差异表达蛋白", 中国烟草学报 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116884478B (en) | 2024-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107679052B (en) | Big data analysis method and mass spectrometry system using the same | |
| CN110444248B (en) | Cancer biomolecule marker screening method and system based on network topology parameters | |
| US8676510B2 (en) | Clinical-result confirming device, clinical-result confirming method, and computer program therefor | |
| CN110033860B (en) | Method for improving detection rate of genetic metabolic diseases based on machine learning | |
| JP5718781B2 (en) | Image classification apparatus and image classification method | |
| CN110890137A (en) | Modeling method, device and application of compound toxicity prediction model | |
| CN104508670A (en) | Systems and methods for generating biomarker signatures | |
| CN108520249A (en) | A kind of construction method of cell sorter, apparatus and system | |
| KR20170059069A (en) | Apparatus and Method for Diagnosis of metabolic disease | |
| CN106874705B (en) | The method for determining tumor marker based on transcript profile data | |
| CN116884478B (en) | Proteomics data analysis method, device, electronic equipment and storage medium | |
| Mazzara et al. | Application of multivariate data analysis for the classification of two dimensional gel images in neuroproteomics | |
| Chen et al. | Evaluating spatially variable gene detection methods for spatial transcriptomics data | |
| US9043249B2 (en) | Automatic chemical assay classification using a space enhancing proximity | |
| CN112798678A (en) | Novel rapid detection method for coronavirus infection based on serum | |
| CN103163257A (en) | Processing method of cow urine iTRAQ test data | |
| CN117191172B (en) | Method and device for mass detection and saturation early warning of balance weighing cover | |
| RU2011117576A (en) | METHOD FOR DETERMINING CERTAIN INDICATOR FOR DISTINCTIVE FEATURES OBTAINED FROM CLINICAL DATA, AND APPLYING THE CERTIFICATE INDICATOR FOR PREFERENCE OF ONE DISTINCTIVE FEATURE OVER OTHER | |
| JP2016502078A (en) | Evaluation system and method for data classification result of flow cytometry | |
| Monakhova et al. | Methodology of chemometric modeling of spectrometric signals in the analysis of complex samples | |
| Lauria | Rank-based miRNA signatures for early cancer detection | |
| CN115376612B (en) | Data evaluation method and device, electronic equipment and storage medium | |
| NL1040872C2 (en) | Method of analysing data from, computer program, computer readable medium and data analysis system. | |
| CN112802546B (en) | Biological state characterization method, device, equipment and storage medium | |
| CN114496089B (en) | Pathogenic microorganism identification method |
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 |