19. Februar 2025

How do biases affect machine-learning models of human single-cell data? And what can we do about it? In their new Perspective article, „Biases in machine-learning models of human single-cell data,“ Theresa Willem, Vladimir A. Shitov, Malte D. Luecken, Niki Kilbertus, Stefan Bauer, Marie Piraud, Alena Buyx, and Fabian Theis explore these pressing questions.

Abstract:

Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are susceptible to biases. Here we discuss various biases that emerge along the pipeline of ML-based single-cell analysis, ranging from societal biases affecting whose samples are collected, to clinical and cohort biases that influence the generalizability of single-cell datasets, biases stemming from single-cell sequencing, ML biases specific to (weakly supervised or unsupervised) ML models trained on human single-cell samples and biases during the interpretation of results from ML models. We end by providing methods for single-cell data scientists to assess and mitigate biases, and call for efforts to address the root causes of biases.

Read the open-source article here: https://www.nature.com/articles/s41556-025-01619-8