Kalen Clifton

Spatial Transcriptomics As Rasterized Image Tensors (STARIT) characterizes cell states with subcellular molecular heterogeneity

STARIT converts spatial transcriptomics data into image tensors for deep learning analysis, enabling subcellular heterogeneity-based cell state identification.

STcompare: comparative spatial transcriptomics data analysis of structurally matched tissues to characterize differentially spatially patterned genes

STcompare identifies differentially spatially patterned genes by comparing spatial correlation and fold-change across structurally matched tissues in spatial transcriptomics data.

Spatial imprints of emergent cardiomyocyte states in the pressure-overloaded heart

The paper uses spatial transcriptomics to analyze cardiomyocyte states in pressure-overloaded mouse hearts, finding pro-fibrotic states linked to microenvironmental changes.

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

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

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics

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.

Alignment of spatial transcriptomics data using diffeomorphic metric mapping

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

The Genetic Insulator RiboJ Increases Expression of Insulated Genes

This study quantifies how the genetic insulator RiboJ increases expression of downstream genes, showing twofold to tenfold increases in protein abundance and twofold increases in transcript abundance depending on promoter strength.