NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data

6Citations
Citations of this article
13Readers
Mendeley users who have this article in their library.

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. 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.

Cite

CITATION STYLE

APA

Abrar, M. A., Kaykobad, M., Rahman, M. S., & Samee, M. A. H. (2023). NoVaTeST: identifying genes with location-dependent noise variance in spatial transcriptomics data. Bioinformatics, 39(6). https://doi.org/10.1093/bioinformatics/btad372

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free