Sample size requirements for machine learning classification of binary outcomes in bulk RNA-Seq data

BACKGROUND: Bulk RNA sequencing data is often leveraged to build machine learning (ML)-based predictive models for classification of disease groups or subtypes, but the sample size needed to adequately train these models is unknown. METHODS: We collected 27 experimental datasets from the Gene Expression Omnibus and the Cancer Genome Atlas. In 24/27 datasets, pseudo-data were simulated using Bayesian Network Generation. Three ML algorithms were assessed: XGBoost (XGB), Random Forest (RF), and Neural Networks (NN). Learning curves were fit, and sample sizes needed to reach the full-dataset AUC minus 0.02 were determined and compared across the datasets/algorithms. Multivariable negative binomial regression models quantified relationships between dataset-level characteristics and required sample sizes within each algorithm. These models were validated in independent experimental datasets. RESULTS: Across the datasets studied, median required sample sizes were 480 (XGB)/190 (RF)/269 (NN). Higher effect sizes, less class imbalance/dispersion, and less complex data were associated with lower required sample size. Validation demonstrated that predictions were accurate in new data. CONCLUSIONS: Comparison of results to sample sizes obtained from differential analysis power analysis methods showed that ML methods generally required larger sample sizes. In conclusion, incorporating ML-based sample size planning alongside traditional power analysis can provide more robust results.