Differential Analysis of Gene Spatial Organisation with Minkowski Functionals and Tensors
Baratta, P.; Villoutreix, P.; Baudot, A.
Show abstract
Spatial transcriptomics measures gene expression together with transcript coordinates in tissues. To date, comparing spatial gene expression patterns within and across samples remains challenging. We present here minkiPy, a geometric framework that computes, for each gene, a compact profile of morphological and topological descriptors based on Minkowski functionals and tensors. These profiles are defined in a shared feature space, enabling direct comparison of spatial organisation across genes, samples, and conditions, and the ranking of genes by the magnitude of their spatial reorganisation. We applied minkiPy to a MERFISH dataset of control and facioscapulohumeral muscular dystrophy myoblast cultures and to a Visium HD dataset of colorectal cancer and normal adjacent tissues, illustrating its utility across tissue types and spatial transcriptomics platforms. minkiPy is an open-source Python library available at https://github.com/BAUDOTlab/minkiPy.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data 97%
- Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data 96%
- geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.