Spatially resolved transcriptomics provides the opportunity to investigate the gene
expression profiles and the spatial context of cells in naive state, but at low transcript …

Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the
transcriptome of individual cells. However, the data from scRNASeq experiments is often …

Spatially resolved gene expression profiling provides insight into tissue organization and
cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell …

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell
types, locations, and intercellular or molecular interactions for physiological and …

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and
dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it …

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in
biomedical research settings, allowing to profile the whole transcriptome of a very large …

Single-cell technologies have transformed biomedical research over the last decade,
opening up new possibilities for understanding cellular heterogeneity, both at the genomic …

Recent advances in spatial transcriptomics (STs) enable gene expression measurements
from a tissue sample while retaining its spatial context. This technology enables …

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data
sources:(1) spatial gene expression data such as image-based fluorescence in situ …

As single-cell RNA sequencing (scRNA-seq) continues to reveal the functional
heterogeneity within tissue types, parallel advances in imaging and spatial analysis …