Language
English
Publication Date
6-1-2023
Journal
Bioinformatics
DOI
10.1093/bioinformatics/btad372
PMID
37285319
PMCID
PMC10283152
PubMedCentral® Posted Date
6-7-2023
PubMedCentral® Full Text Version
Post-print
Abstract
Motivation: Spatial transcriptomics (ST) can reveal the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify spatially localized processes underlying a tissue's function. Existing tools to detect spatially variable genes assume a constant noise variance across spatial locations. This assumption might miss important biological signals when the variance can change across locations.
Results: In this article, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST models gene expression as a function of spatial location and allows the noise to vary spatially. NoVaTeST then statistically compares this model to one with constant noise and detects genes showing significant spatial noise variation. We refer to these genes as "noisy genes." In tumor samples, the noisy genes detected by NoVaTeST are largely independent of the spatially variable genes detected by existing tools that assume constant noise, and provide important biological insights into tumor microenvironments.
Keywords
Software, Transcriptome, Gene Expression Profiling
Published Open-Access
yes
Recommended Citation
Abrar, Mohammed Abid; Kaykobad, M; Rahman, M Saifur; et al., "NoVaTeST: Identifying Genes With Location-Dependent Noise Variance in Spatial Transcriptomics Data" (2023). Faculty and Staff Publications. 4021.
https://digitalcommons.library.tmc.edu/baylor_docs/4021
Comments
Availability and implementation: An implementation of the NoVaTeST framework in Python along with instructions for running the pipeline is available at https://github.com/abidabrar-bracu/NoVaTeST.