Biases in machine-learning models of human single-cell data.

Area of Science:

• Computational Biology
• Genomics
• Biomedical Data Science

Background:

• Machine learning (ML) advances enable single-cell data analysis for donor stratification and insights.
• Single-cell resolution analysis holds promise for diagnostics and prognostics.
• However, ML-based single-cell insights are vulnerable to significant biases.

Purpose of the Study:

• To comprehensively discuss biases in ML-based single-cell analysis pipelines.
• To identify bias sources from sample collection to result interpretation.
• To propose strategies for bias assessment and mitigation in single-cell data science.

Main Methods:

• Review and categorization of biases across the single-cell ML pipeline.
• Analysis of societal, clinical, cohort, sequencing, ML model, and interpretation biases.
• Identification of mitigation strategies for data scientists.

Main Results:

• Biases are introduced at multiple stages: societal, clinical, cohort, sequencing, ML model training, and interpretation.
• Specific biases include sample collection disparities, generalizability issues, sequencing artifacts, and model-specific limitations.
• Methods for assessing and mitigating these biases are presented.

Conclusions:

• Addressing biases is crucial for the reliable application of ML in single-cell analysis.
• Mitigation strategies and efforts to tackle root causes are essential for trustworthy diagnostic and prognostic insights.
• Further research and community efforts are needed to ensure fairness and accuracy.