Bayesian Clustering Approach with Feature Selection for Analyzing Spatially Resolved Transcriptomics Data
- Developed Bayesian spatial clustering methods for high-dimensional count data, achieving more robust and accurate results in both simulatd and real datasets
- Utilized feature selection to identifiy discriminating genes, improving model performance and interpretability
- Implemented Markov Chain Monte Carlo (MCMC) methods with R and C++
- Link
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Robust Bayesian Integrative Modeling of Single Cell and Spatial Transcriptomics Data
- Integrating spatial transcriptomics (ST) data with scRNA-seq reference data
- Decomposing cell-type mixtures of regularly distributed spots and identifying the underlying spatial domains simultaneously
- Capturing cell type sparsity using a zero-inflated Dirichlet distribution
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Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research
- Involved in the website design with curated datasets, reproducible methods, and analysis results to facilitate the spatial transcriptomics methodology research
- Collected datasets, including data retrieval, categorization, organization and pre-processing
- Selected spatially variable gene identification methods and applied them on simulated and real data
- Developed and maintained an R package boost as an integrated tool of existing spatially variable gene identification methods
- bioRxiv
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BACON: Bayesian Clustering of n-gons via a Double Dirichlet Mixture Model
- Developing a landmark-based Bayesian clustering method for closed polygonal chains, relying on intrinsic shape features
- Employing a weighted version of a Double Truncated Dirichlet (DTDir) Model to account for geometric constraints on a simplex space
- Employing a Mixture of Finite Mixtures (MFM) model to estimate the optimal number of clusters
- GitHub
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South Texas Early Prevention Study (STEPS) Pre-K Project
- Designed a cluster randomized trial to assess the effect of the Bienestar coordinated school health program on children’s health outcomes
- Examined social and health risk factors among preschool children living along the Texas-Mexico border, a region with high poverty and limited access to healthcare
- Collected family characteristics, dietary intake, fitness, and anthropometric data from 1277 preschool students enrolled in 28 preschools
- Revealed that children living in low-income areas are affected by high levels of social and health risk factors
- Link
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