Conformal inference for cell type annotation with graph-structured constraints

Conformal prediction is a framework for constructing prediction sets for machine learning models, relying solely on the exchangeability of training and test data and without requiring to specify a parametric distribution. Despite its wide applicability and popularity, its application in single-cell transcriptomics remains underexplored. This paper addresses this gap by developing an approach that leverages the rich information about cell-type relations, encoded in the graph structure of cell ontologies, to enhance the interpretability of reference-based cell-type annotation. Leveraging conformal risk control, we develop a novel conformal algorithm for graph-structured predictions and we demonstrate how incorporating graph constraints can improve the interpretation of cell-type predictions. This approach aims to generate more coherent conformal sets that align with the inherent relationships among classes, facilitating clearer and more intuitive interpretations of model predictions. Additionally, we provide a technique to address non-exchangeability, particularly when the cell-type distribution changes between training and test datasets. We implemented our method in the open-source R package scConform, available at https://bioconductor.posit.co/packages/release/bioc/html/scConform.html.