Identifying Genes with Location Dependent Noise Variance in Spatial Transcriptomics Data

Abstract

Spatial transcriptomics (ST) holds the promise to identify the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify gene expression signatures that distinguish between healthy and disease samples. Existing tools to detect spatially variable genes assume a constant noise variance across location. This assumption might miss important biological signals when the variance could change across spatial locations, e.g., in the tumor microenvironment. In this paper, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST can model gene expression as a function of spatial location with a spatially variable noise. We then compare the model to one with constant noise to detect genes that show significant spatial variation in noise. Our results show genes detected by NoVaTeST provide complimentary information to existing tools while providing important biological insights.