The Bayesian Sparse Gaussian Mixture Model (BSGMM) is designed for clustering in high-dimensional data where the number of clusters can grow with the sample size. This model addresses the challenge of parameter estimation in high dimensions by establishing a minimax lower bound and achieving it with a constrained maximum likelihood estimator. However, due to the nonconvex nature of the objective function, this estimator is computationally intractable. To overcome this, a Bayesian approach is proposed, utilizing a continuous spike-and-slab prior to estimate high-dimensional Gaussian mixtures with sparse cluster centers. The model's posterior contraction rate is shown to be minimax optimal, and it does not require pre-specifying the number of clusters, which can be adaptively estimated. The BSGMM is validated through simulation studies and applied to real-world single-cell RNA sequencing data, demonstrating its effectiveness in capturing complex data structures.
Bayesian inference, spike-and-slab prior
Gaussian mixture model
Single-cell RNA sequencing data
Clustering accuracy, posterior contraction rate
Cloud-based, on-premises
Yes
Yes
Adaptive clustering, sparse cluster centers, Bayesian inference
Yes
High-performance computing resources
Linux, Windows, macOS
Compatible with statistical software
Data encryption, access control
GDPR, HIPAA
None
No
Limited community support
Academic researchers
Large datasets
Moderate
Moderate
Graphical representation of clusters
Ensuring data privacy and ethical use of clustering models
Computational complexity in high dimensions
Healthcare, genomics, data science
Clustering in genomics, bioinformatics analysis
Research institutions, universities
API integration with data analysis tools
Scalable with computational resources
Academic support, consulting services
None
Command-line interface, graphical user interface
Yes
English
Subscription-based, academic licensing
Yes
Collaborations with research institutions
None
Compliant with research ethics guidelines
1.0
Software
Yes
RESTful API for integration
Academic and research-focused
0.00
USD
Academic license
01/01/2023
01/10/2023
+1-800-555-0199
Integration with statistical software, support for complex clustering models
Yes