Bayesian Methods and Mixture Models

Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference

I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data

I-SVVS improves clustering accuracy and reduces computation time for multi-omics microbiome data across soybean, mice, and human datasets.

Clustering large-scale biomedical data to model dynamic accumulation processes in disease progression and anti-microbial resistance evolution

The paper introduces a clustering-based method to model dynamic accumulation processes in disease progression and antimicrobial resistance evolution using hypercubic hidden Markov models.

Spatially aligned random partition models on spatially resolved transcriptomics data

The paper introduces SARP models to cluster spatially resolved transcriptomics data and identifies co-localized immune and stromal cell subtypes in colorectal cancer.

Immune Profiling among Colorectal Cancer Subtypes using Dependent Mixture Models

The paper develops dependent mixture models to compare immune cell subtypes between early-onset and late-onset colorectal cancer, identifying distinct and shared T cell profiles.

Multiple latent clustering model for the inference of RNA life-cycle kinetic rates from sequencing data

The paper introduces a Bayesian model to infer RNA life-cycle kinetic rates from sequencing data and applies it to study Myc activation in murine fibroblasts, revealing new regulatory patterns.

Bayesian clustering with uncertain data

This paper introduces DPMUnc, a Bayesian clustering method that incorporates data uncertainty, and applies it to GWAS and gene expression data to identify disease subgroups.

Dissecting unsupervised learning through hidden Markov modelling in electrophysiological data

The paper evaluates how different assumptions in hidden Markov models affect state decomposition in electrophysiological data using synthetic and real data.

Stochastic variational variable selection for high-dimensional microbiome data

The paper introduces SVVS, a method for analyzing high-dimensional microbiome data, showing improved performance and faster computation compared to existing methods.

Consensus clustering for Bayesian mixture models

The paper introduces a consensus clustering method for Bayesian mixture models, showing it can uncover target clustering structures and handle large datasets efficiently.

MGMM: An R Package for fitting Gaussian Mixture Models on Incomplete Data

MGMM fits Gaussian mixture models on incomplete data, outperforming imputation methods in recovering cluster assignments and assessing uncertainty.

Integrating Sample Similarities into Latent Class Analysis: A Tree-Structured Shrinkage Approach

The paper develops a tree-structured shrinkage method for latent class analysis that integrates sample similarities to improve class probability estimation.

A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data

This paper introduces a Bayesian nonparametric model to infer subclonal populations from structured DNA sequencing data, showing improved performance over existing methods.

A Bayesian Approach to Restricted Latent Class Models for Scientifically-Structured Clustering of Multivariate Binary Outcomes

The paper develops a Bayesian method for clustering multivariate binary data with scientific constraints, showing improved interpretability and clustering performance in protein data analysis.

Model-based cell clustering and population tracking for time-series flow cytometry data

CYBERTRACK is a statistical framework for clustering and tracking cell populations in time-series flow cytometry data, showing consistent results with known lymphocyte dynamics.

Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers

The paper introduces a semi-supervised clustering model for identifying active enhancers in mouse heart tissues using heterogeneous genomic data.

A Latent Allocation Model for the Analysis of Microbial Composition and Disease

This paper introduces BERMUDA, a probabilistic model for analyzing microbial composition and disease, showing improved handling of zero-inflated data and providing efficient learning through an annealing EM algorithm.

2-Way k-Means as a Model for Microbiome Samples

The paper introduces a 2-way k-means model for clustering microbiome samples that can handle mixed compositions between clusters, applied to 16S rDNA sequencing data from the Human Vaginal Microbiome Project.