Metagenomic Feature Selection Method Based on Variable Importance Score and Neyman-Pearson Test

Abstract

The invention discloses a metagenomic feature selection method based on variable importance score and Neyman-Pearson test. 1. For an input metagenomic classification operable unit data set, use symmetric uncertainty to calculate the relationship between each microbial feature and The correlation of the sample phenotype, filter the features according to the correlation score, and generate a sub-dataset; 2. Sampling the sub-dataset with replacement sampling, and then use the variable importance score to select the top k features, iterate the above steps, After the iteration is completed, the number of occurrences of each feature is counted; 3. Use the Neyman-Pearson test method to calculate the threshold under the given parameters, and select the features whose occurrences are greater than the threshold as the candidate feature set, and the top k features with the most occurrences are the target Feature subset: the metagenome feature extracted by the present invention significantly improves the classification effect and has higher stability, and the generated candidate feature set facilitates the development of subsequent medical experiments of the metagenomic group.

Description

technical field [0001] The invention belongs to the field of analysis of metagenomic abundance data, and in particular relates to a method for selecting metagenomic features based on variable importance scores and Neyman-Pearson tests. Background technique [0002] The core problem of metagenomic abundance data analysis is to effectively identify a small number of microorganisms that have potential effects on phenotypes from a large number of microorganisms, which is widely used in medicine, biology, environmental science, food science and other disciplines. The metagenomic data set contains a wide variety of microorganisms, and it takes a lot of work to directly study the impact of all microorganisms on the phenotype. Therefore, it is necessary to use other methods to remove the noise of the original data set and leave the microorganisms that have the potential to affect the phenotype, that is, macro. Feature selection for genomic data. [0003] In order to effectively ide...

AI Technical Summary

Problems solved by technology

However, metagenomic abundance data contains a large number of irrelevant features, and the feature importance score of random forest is easily affected by noise.

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