A Novel Filter and Embedded Feature Selection Methods applied to High Dimensional Metabolomics Data in Enhancing Colorectal Cancer Classification

Abstract

Abstract Background Metabolomics is an emerging field, which focuses on the study of small molecules (metabolites) and their chemical processes. Metabolomics data are highly dimensional, with p>>n where p is the number of variables and n is the sample size of the cohort. Hence, feature selection is a key step in metabolomics studies to reduce the dimensionality in the dataset, removing redundant and unwanted features and mitigating overfitting. The t-test (T) and correlation sharing t-test method (corT) can be used as filter methods. Penalized regression, and in particular the embedded method least absolute shrinkage and selection operator (Lasso), have also been applied for feature selection with the aim of minimising the problem of overfitting that often affects prediction models in this field. These methods are here applied to datasets of volatile organic compounds (VOCs) from patients with colorectal cancer or non-cancer (aimed at discriminating between non-cancer vs colorectal cancer groups, and healthy control vs adenoma groups). Cross validation, with data split into two sets (80% for training and 20% for validation) was used to compare the performance of the feature selection methods in terms of classification accuracy, sensitivity, specificity, and area under ROC. Results As a result, for non-cancer and colorectal cancer discrimination, the T method showed the worst classification accuracy followed by Lasso. CorT achieved the best level of discrimination although this was still low (AUC of 0.60). For healthy control and adenoma discrimination however, methods corT showed the lowest AUC, followed by the T method. Lasso achieved the best level of discrimination, although this remained low (AUC of 0.65). However, there is limitation of these methods where the feature selection methods considered were not able to identify a set of VOCs with good levels of discrimination between colon cancer, adenoma and control cases. Conclusion This paper is being limited use to assist medical practitioners in earlier detection of colorectal cancer.