Fluoro-forest: A random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging.

ABSTRACT: Cyclic immunofluorescence (IF) techniques enable deep phenotyping of cells and help quantify tissue organization at high resolution. Due to its high dimensionality, workflows typically rely on unsupervised clustering, followed by cell type annotation at a cluster level for cell type assignment. Most of these methods use marker expression averages that lack a statistical evaluation of cell type annotations, which can result in misclassification. Here, we propose a strategy through an end-to-end pipeline using a semi-supervised, random forests approach to predict cell type annotations. Our method includes cluster-based sampling for training data, cell type prediction, and downstream visualization for interpretability of cell annotation that ultimately improves classification results. We show

SUBMITTER: Brand J 

PROVIDER: S-EPMC12224550 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature