PMID- 34146105 OWN - NLM STAT- In-Process VI - 37 IP - 22 TI - Bayesian modeling of spatial molecular profiling data via Gaussian process. PG - 4129-4136 CI - © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. LA - eng PT - Journal Article PL - England TA - Bioinformatics JT - Bioinformatics (Oxford, England) JID - 9808944 IS - 1367-4811 (Electronic) LID - 10.1093/bioinformatics/btab455 [doi] FAU - Li, Qiwei AU - Li Q AUID- ORCID: 0000-0002-1020-3050 AD - Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA. FAU - Zhang, Minzhe AU - Zhang M AD - Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. FAU - Xie, Yang AU - Xie Y AD - Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. FAU - Xiao, Guanghua AU - Xiao G AD - Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. IS - 1367-4803 (Linking) SB - IM PMC - PMC9502169 LR - 20230412 DP - 2021 Nov 18 AB - 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 binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. AVAILABILITY AND IMPLEMENTATION: The related R/C++ source code is available at https://github.com/Minzhe/BOOST-GP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.