Automated benchmarking and selection of clustering algorithms (KMeans, Agglomerative, Spectral, GMM, DBSCAN) for spatial/multiplex immunofluorescence data, using Hopkins statistic, PCA, multi-metric evaluation (Silhouette, Davies-Bouldin, Calinski-Harabasz, ARI stability) and automatic best-method selection.
Python-based pipeline for segmentation quality control and unsupervised clustering of immunofluorescence spatial-omics data.
Quality control pipeline for MACSima / MACSiQView cell segmentation
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