Bayesian modeling of spatial molecular profiling data via Gaussian process.

ABSTRACT:

Motivation

The location, timing and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns.

Results

We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binom

SUBMITTER: Li Q 

PROVIDER: S-EPMC9502169 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature