Differential expression analysis for spatially correlated data using smiDE
Abstract
Differential expression is a key application of imaging spatial transcriptomics, moving analysis beyond cell type localization to examining cell state responses to microenvironments. However, spatial data poses new challenges to differential expression: segmentation errors cause bias in fold-change estimates, and correlation among neighboring cells leads standard models to inflate statistical significance. We find that ignoring these issues can result in considerable false discoveries that greatly outnumber true findings. We present a suite of solutions to these fundamental challenges, and implement them in the R package smiDE.
Fields of Study & Subjects
Single-cell and spatial transcriptomics
Cell Image Analysis Techniques
Point processes and geometric inequalities
Publication details
- Journal / source
- Genome biology
- Publisher
- Springer Science and Business Media LLC
- Volume
- 27
- Issue
- 1
- Pages
- 21–21
- Publication date
- 10 January 2026
- Language
- EN