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. 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. 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. 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.

The authors have declared no competing interest.

This study did not receive any funding

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The study used (or will use) ONLY openly available human data that were originally located at: https://www.ncbi.nlm.nih.gov/geo/ AND https://www.cancer.gov/ccg/research/genome-sequencing/tcga

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All datasets used in this study are publicly available and can be found at https://www.ncbi.nlm.nih.gov/geo/. Furthermore, R Code used for further pre-processing and analysis is available from the corresponding author on request. Following acceptance, an R Shiny app will be made publicly available, where users can input dataset-level variables and receive training set size recommendations based on the results of this study.