Vrednotenje napovedovanja transkriptoma posameznih celic na podlagi biološkega prenosa znanja med vzorci

This study explores the evaluation of perturbation prediction models in single-cell RNA sequencing through the application of alternative metrics, beyond the commonly used correlation and R-squared. Nine evaluation metrics, cosine distance, Euclidean distance, log-likelihood, maximum mean discrepancy, mean absolute error, mean squared error, Pearson’s R, R-squared, and root mean squared error, were applied to both real and simulated datasets to assess their ability to capture differences between control, perturbed, and predicted conditions. We observed that while alternative metrics showed some variation in behavior, they did not consistently outperform traditional metrics. Furthermore, the study investigates the impact of gene expression variability, using a binning strategy, and identifies biases introduced by grouping genes with similar expression strengths. These findings highlight the importance of data variability and the potential limitations of the binning approach in perturbation prediction evaluations. Our research suggests that traditional metrics, such as R-squared and Pearson’s R, are adequate for evaluating scRNA-seq perturbation models in controlled settings. However, future work could benefit from exploring more diverse datasets, alternative models, and refined binning strategies to further examine this statement.