Optimizing Sample Size for Supervised Machine Learning with Bulk Transcriptomic Sequencing: A Learning Curve Approach

TL;DR

This paper introduces a new computational method to determine the optimal sample size for supervised machine learning in transcriptomics, using a learning curve approach to improve personalized medicine applications.

Contribution

It presents a novel data augmentation and learning curve fitting technique specifically designed for sample size estimation in transcriptomics-based machine learning.

Findings

Effective across microRNA and RNA sequencing data

Performs well with diverse data characteristics

Accessible Python and R implementations available

Abstract

Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate statistical power without undue resource allocation. Current sample size calculation methods rely on assumptions and algorithms that may not align with supervised machine learning techniques for sample classification. Addressing this critical methodological gap, we present a novel computational approach that establishes the power-versus-sample-size relationship by employing a data augmentation strategy followed by fitting a learning curve. We comprehensively evaluated its performance for microRNA and RNA sequencing data, considering diverse data characteristics and algorithm configurations, based on a spectrum of evaluation metrics. To foster accessibility and reproducibility, the Python and R code for implementing our approach is available on GitHub. Its deployment will significantly facilitate the adoption of machine learning in transcriptomics studies and accelerate their translation into clinically useful classifiers for personalized treatment.