Cancer subtype classification method based on multi-omics integration

A classification method and omics technology, applied in the field of cancer subtype classification based on multi-omics integration, can solve the problems of large error, no consideration of sample similarity bias, omics data weight, poor accuracy of cancer subtype classification results, etc.

Active Publication Date: 2020-06-16
SHENZHEN INST OF ADVANCED TECH
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the existing method does not consider the similarity deviation between samples and the weight of different omics data in the integration, resulting in poor accuracy and large errors in the classification results of cancer subtypes of patients

Method used

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  • Cancer subtype classification method based on multi-omics integration
  • Cancer subtype classification method based on multi-omics integration
  • Cancer subtype classification method based on multi-omics integration

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Experimental program
Comparison scheme
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Embodiment 1

[0066] refer to figure 2 , the first embodiment of the present invention provides a cancer subtype classification method based on multi-omics integration, including:

[0067] Step S10, acquiring target multi-omics data of each patient in the target cancer patient group; and calculating an omics similarity matrix corresponding to each omics in the target multi-omics data;

[0068] As mentioned above, the omics similarity matrix is ​​M k ; among them, M k Can be expressed in the following form:

[0069]

[0070] As mentioned above, the target cancer patient group is a collection of batch cancer subtype classification for all patients in the group that need to be analyzed. The target cancer patient group includes pathological data (physical and chemical index data, biochemical test results, etc.) of patients with the same type of cancer but the same and or different conditions.

[0071] As mentioned above, the target cancer patient group includes multiple patients with th...

Embodiment 2

[0093] refer to image 3 , the second embodiment of the present invention provides a cancer subtype classification method based on multi-omics integration, based on the above figure 2 In the first embodiment shown, the step S20 of "predicting each of the omics similarity matrices using a linear regression method to obtain a predicted similarity matrix corresponding to each of the omics similarity matrices" includes:

[0094] Step S21, based on the linear regression method, respectively use the omics similarity matrix corresponding to each omics of the target multi-omics data of each patient as the target matrix, and use the omics similarity matrix corresponding to other omics to pair performing linear regression prediction on the target matrix, respectively obtaining predicted values ​​corresponding to data in each of the target matrices of the target multi-omics data, and obtaining each of the omics similarity matrices containing the predicted values The corresponding predi...

Embodiment 3

[0111] refer to Figure 4-5 , the third embodiment of the present invention provides a cancer subtype classification method based on multi-omics integration, based on the above figure 2 In the first embodiment shown, the step S10, "acquire the target multi-omics data of each patient in the target cancer patient group; and calculate the group corresponding to each omics in the target multi-omics data Learning similarity matrix" includes:

[0112] Step S11, determining the target multi-omics data of each patient in the target cancer patient group, and performing mean interpolation on data corresponding to missing omics;

[0113] As mentioned above, in the target multi-omics data, there are multiple omics, but due to the large number of patients, not every patient has completed the test of each omics, and there may be missing tests, resulting in some patients missing In the case that a certain omics cannot be calculated, it is necessary to interpolate the missing omics of the ...

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Abstract

The invention provides a cancer subtype classification method based on multi-omics integration. The method comprises the steps of obtaining target multi-omics data of each patient in a target cancer patient group; calculating to obtain an omics similarity matrix; predicting each omics similarity matrix to obtain a predicted similarity matrix; correcting the prediction similarity matrix by utilizing the omics similarity matrix to obtain a correction matrix; performing weighted fusion to obtain a fusion matrix; and performing spectral clustering on the fusion matrix, and establishing a cancer subtype category label corresponding to the fusion matrix of each patient. The accuracy of classification and evaluation of the cancer subtypes is improved, the patients are classified through a more flexible integration method, the data analysis efficiency is improved, and convenience is provided for research on the cancer subtypes.

Description

technical field [0001] The present invention relates to the technical field of cancer subtype classification and evaluation, and more specifically, relates to a cancer subtype classification method based on multi-omics integration. Background technique [0002] Identification of cancer subtypes is crucial for cancer diagnosis and treatment. There is an unbalanced classification of cancer subtypes using only single-omics information, and often the divided cancer subtypes have large differences in survival rates. Therefore, many approaches to identify cancer subtypes by integrating targeted multi-omics data have been proposed in recent years. [0003] Common methods for cancer target multi-omics data integration include feature extraction, dimensionality reduction, and similarity matrix calculation, among which feature extraction and dimensionality reduction methods are generally used in combination, such as latent variable factorization. Commonly used clustering methods are...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/23G16B40/00G06F18/00Y02A90/10
Inventor 杨超殷鹏蒋佳新
Owner SHENZHEN INST OF ADVANCED TECH
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