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Related Experiment Video

Updated: Sep 3, 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

A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data.

Xi Jiang^1,2, Guanghua Xiao², Qiwei Li³

¹Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA.

Statistics in Medicine

|July 25, 2022

Summary

This summary is machine-generated.

A new Bayesian method using a modified Ising model accurately identifies spatially variable (SV) genes in spatial molecular profiling data. This approach enhances the detection of complex spatial expression patterns, offering new insights into tissue structure and function.

Keywords:

dichotomization double Metropolis-Hastings multitype point pattern spatial correlation spatial molecular profiling

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Last Updated: Sep 3, 2025

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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial molecular profiling (SMP) enables single-cell molecular characterization with spatial context.
Identifying spatially variable (SV) genes is crucial for understanding tissue organization and function.
Existing geostatistical methods using Gaussian processes have limitations in capturing complex spatial patterns due to ad hoc kernels.

Purpose of the Study:

To develop a novel Bayesian approach for identifying SV genes in SMP data.
To overcome the limitations of kernel-based methods in capturing intricate spatial expression patterns.
To provide a new perspective for analyzing spatial transcriptomics (ST) data.

Main Methods:

Introduced a Bayesian approach utilizing a modified Ising model to characterize spatial expression patterns via energy interaction parameters.
Employed auxiliary variable Markov chain Monte Carlo algorithms for posterior distribution sampling, addressing intractable normalizing constants.
Validated the method using simulated, synthetic, and real spatial transcriptomics datasets.

Main Results:

The energy-based modeling approach demonstrated higher accuracy in detecting SV genes compared to traditional kernel-based methods.
The method successfully identified novel spatial patterns in real ST datasets.
These discovered patterns offer potential insights into underlying biological mechanisms.

Conclusions:

The proposed modified Ising model offers a powerful and accurate method for SV gene identification in SMP data.
This Bayesian approach expands the capability to detect complex spatial expression patterns.
The method provides a valuable new perspective for the analysis of spatial transcriptomics data.