Fluoro-Forest CODEX data for random Forest-based cell type annotation

High-plex immunofluorescence (IF) 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 that our workflow can annotate cells more
accurately with a training set < 5 % of the total number of cells
tested. In addition, our pipeline outputs cell type annotation
probabilities and model performance metrics for users to decide if it
could boost their existing clustering-based workflow results for complex
IF data.