High Dimensional Classification with combined Adaptive Sparse PLS and Logistic Regression

TL;DR

This paper introduces a stable, convergent high-dimensional classification method combining adaptive sparse PLS and logistic regression, improving stability and interpretability in genomic data analysis.

Contribution

It proposes a new stable, convergent approach for high-dimensional classification using adaptive sparse PLS integrated with logistic regression, addressing instability issues in previous methods.

Findings

Method is stable and convergent on synthetic and real data.

Improves prediction of breast cancer relapse from gene expression.

Extends to multicategorical classification for cell-type prediction.

Abstract

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to instable and non convergent methods due to inappropriate computational frameworks. We hereby propose a stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, which combines iterative optimization of logistic regression and sparse PLS to ensure convergence and stability. Our results are confirmed on synthetic and experimental data. In particular we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method on the prediction of cell-types based on single-cell expression data. Availability: Our approach is implemented in the plsgenomics R-package.