2DE: a probabilistic method for differential expression across niches in spatial transcriptomics data

Nathan Levy, Florian Ingelfinger, Artemy Bakulin, Giacomo Cinnirella, Pierre Boyeau, Can Ergen, Nir Yosef

Track: Tiny paper track (up to 4 pages)

Abstract:

Spatial transcriptomics enables studying cellular interactions by measuring gene
expression in situ while preserving tissue context. Within tissues, distinct cellular
niches define micro-environments that influence cell states and function. A fundamental task in spatial transcriptomics is identifying differentially expressed genes
within a specific cell type across different niches to quantify context-dependent
cell state variation. Despite advances in cell segmentation algorithms, the persisting problem of the wrong assignment of molecules to cells can obscure the analysis by introducing spurious differentially expressed genes that originate from
neighboring cells rather than the group of interest. Here, we introduce 2DE, a
probabilistic framework designed to refine spatial differential expression analyses
by filtering out genes that are over-expressed due to local contamination rather
than true cell-intrinsic expression. 2DE operates downstream of any differential
expression method, filtering irrelevant genes by considering gene over-expression
relative to the expression in the neighborhood and returning marker confidence
scores. In a study of human breast cancer, we demonstrate that 2DE improves the
precision of the discoveries.

Submission Number: 43