Lyla Atta

The paper introduces CRAWDAD, a tool for analyzing cell-type spatial relationships across scales in spatially resolved omics data, and demonstrates its application on simulated and real datasets.

SEraster reduces computational resource needs for spatial omics analysis by aggregating cell data into spatial pixels, enabling efficient analysis of large datasets.

This study evaluates how different normalization methods affect the analysis of spatially resolved transcriptomics data, showing that gene count-based normalization can introduce biases in downstream analyses.

This paper introduces STalign, a method for aligning spatial transcriptomics data using diffeomorphic metric mapping, improving alignment accuracy over affine methods.

This paper introduces STdeconvolve, a reference-free method for cell-type deconvolution of spatially resolved transcriptomics data at multi-cellular pixel resolution, showing effective recovery of cell-type profiles without external references.

VeloViz creates RNA-velocity-informed 2D and 3D embeddings to visualize cellular trajectories from single-cell transcriptomics data, showing consistent results across diverse topologies.