Multi-TGDR: a regularization method for multi-class classification in microarray experiments

TL;DR

This paper introduces Multi-TGDR, an extension of the TGDR algorithm for multi-class microarray data classification, improving predictive accuracy and stability while reducing computational complexity.

Contribution

It develops a multi-class regularization method, Multi-TGDR, and an explicit approach for applying Meta-TGDR to independent samples, enhancing microarray classification.

Findings

Multi-TGDR selects fewer genes than binary TGDRs.

Meta-TGDR and TGDR perform similarly on adjusted data.

Adding Bagging improves stability and prediction accuracy.

Abstract

Background With microarray technology becoming mature and popular, the selection and use of a small number of relevant genes for accurate classification of samples is a hot topic in the circles of biostatistics and bioinformatics. However, most of the developed algorithms lack the ability to handle multiple classes, which arguably a common application. Here, we propose an extension to an existing regularization algorithm called Threshold Gradient Descent Regularization (TGDR) to specifically tackle multi-class classification of microarray data. When there are several microarray experiments addressing the same/similar objectives, one option is to use meta-analysis version of TGDR (Meta-TGDR), which considers the classification task as combination of classifiers with the same structure/model while allowing the parameters to vary across studies. However, the original Meta-TGDR extension did not offer a solution to the prediction on independent samples. Here, we propose an explicit method to estimate the overall coefficients of the biomarkers selected by Meta-TGDR. This extension permits broader applicability and allows a comparison between the predictive performance of Meta-TGDR and TGDR using an independent testing set. Results Using real-world applications, we demonstrated the proposed multi-TGDR framework works well and the number of selected genes is less than the sum of all individualized binary TGDRs. Additionally, Meta-TGDR and TGDR on the batch-effect adjusted pooled data approximately provided same results. By adding Bagging procedure in each application, the stability and good predictive performance are warranted. Conclusions Compared with Meta-TGDR, TGDR is less computing time intensive, and requires no samples of all classes in each study. On the adjusted data, it has approximate same predictive performance with Meta-TGDR. Thus, it is highly recommended.