BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding. Supplementary data.

We propose a Bayesian Reduction for Amplified Quantization in Umap Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, able to enhance input fragmentation by fitting a lognormal mixture model and shrinking each component towards its median, in order to help further clustering step in finding more separated and clear clusters. The BRAQUE’s pipeline consist of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. These SUPPLEMENTAL DATA contain the csv image data files for seven lymphoid tissues, the antibody list and an MTA agreement letter for the CyBorgh software.