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.
Steps to reproduce
See the related manuscript.
POR FESR 2014–2020, Call HUB Ricerca 557 ed Innovazione: ImmunHUB
Additional Metadata for University of Milano - Bicocca
|Date the data was collected||2022-11-29T00:00:00.000Z|
|ERC Keywords||PE1_16 Mathematical aspects of computer science, PE6_2 Computer systems, parallel/distributed systems, sensor networks, embedded systems, cyber-physical systems, PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)|
|SSD Classification||FIS/02 - FISICA TEORICA, MODELLI E METODI MATEMATICI, INF/01 - INFORMATICA, MED/04 - PATOLOGIA GENERALE, MED/08 - ANATOMIA PATOLOGICA|