Conformal inference for reliable single cell RNA-seq annotation.

ABSTRACT:

Motivation

Despite the inherent complexity associated to automatic cell type assignments, most supervised learning models overlook rigorous uncertainty quantification on the annotations. Although some existing pipelines incorporate rejection options under predefined circumstances, they usually rely on arbitrary assumptions and do not provide statistical guarantees. In this work, we propose a methodology based on the conformal prediction framework to provide reliable single-cell annotations. Conformal prediction provides statistical guarantees on the outcome predictions without making any assumption about the underlying distribution of the data. Our methodological proposal leverages conformal inference to address two critical challenges in single-cell RNA sequencing annotations: (i)

SUBMITTER: Lopez-De-Castro M 

PROVIDER: S-EPMC12506889 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature