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Related Experiment Video

Updated: Jul 6, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved

Jie Yang¹, Xi Jiang², Kevin W Jin³

¹Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A.

Biorxiv : the Preprint Server for Biology

|January 3, 2024

Summary

This summary is machine-generated.

This study introduces a new Bayesian framework for analyzing spatially resolved transcriptomics data. The method accurately identifies spatially variable genes, overcoming limitations of existing approaches for irregular cell distributions.

Keywords:

Bayesian mark interaction model Zero-inflated negative binomial mixture model double Metropolis-Hastings algorithm energy function spatial transcriptomics

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

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Last Updated: Jul 6, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) enables cell molecular characterization with spatial context.
Identifying spatially variable genes is crucial for understanding tissue organization.
Existing methods have limitations with irregular cell distributions or lattice-based assumptions.

Approach:

Developed a generalized energy-based Bayesian framework for imaging-based SRT data.
Utilized a zero-inflated negative binomial mixture model for noise reduction.
Incorporated a geostatistical mark interaction model with a generalized energy function.

Key Points:

The framework accommodates irregular spatial distributions of cells.
Auxiliary variable Markov chain Monte Carlo (MCMC) algorithms were used for posterior sampling.
Demonstrated high accuracy in capturing various spatial patterns on simulated data.

Conclusions:

The proposed method successfully identifies genes with novel and strong spatial patterns.
Validated on seqFISH and STARmap datasets, outperforming existing approaches.
Advances the analysis of spatial gene expression patterns in complex biological systems.