Principles and challenges of modeling temporal and spatial omics data
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …
and spatial dependencies underlying a biological process or system. With advances in high …
[HTML][HTML] Spatial epigenome–transcriptome co-profiling of mammalian tissues
Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics,
are becoming powerful tools for profiling of cellular states in the tissue context,,,–. However …
are becoming powerful tools for profiling of cellular states in the tissue context,,,–. However …
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Background Spatial transcriptomics (ST) is advancing our understanding of complex tissues
and organisms. However, building a robust clustering algorithm to define spatially coherent …
and organisms. However, building a robust clustering algorithm to define spatially coherent …
Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics
Cells do not live in a vacuum, but in a milieu defined by cell–cell communication that can be
measured via emerging high-resolution spatial transcriptomics approaches. However …
measured via emerging high-resolution spatial transcriptomics approaches. However …
A count-based model for delineating cell–cell interactions in spatial transcriptomics data
Abstract Motivation Cell–cell interactions (CCIs) consist of cells exchanging signals with
themselves and neighboring cells by expressing ligand and receptor molecules and play a …
themselves and neighboring cells by expressing ligand and receptor molecules and play a …
[HTML][HTML] Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics
Spatial transcriptomics techniques, while measuring gene expression, retain spatial location
information, aiding in situ studies of organismal tissue architecture and the progression of …
information, aiding in situ studies of organismal tissue architecture and the progression of …
Mapping the topography of spatial gene expression with interpretable deep learning
Spatially resolved transcriptomics technologies provide high-throughput measurements of
gene expression in a tissue slice, but the sparsity of this data complicates the analysis of …
gene expression in a tissue slice, but the sparsity of this data complicates the analysis of …
spVC for the detection and interpretation of spatial gene expression variation
Spatially resolved transcriptomics technologies have opened new avenues for
understanding gene expression heterogeneity in spatial contexts. However, existing …
understanding gene expression heterogeneity in spatial contexts. However, existing …
Categorization of 31 computational methods to detect spatially variable genes from spatially resolved transcriptomics data
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes
(SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and …
(SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and …
[HTML][HTML] ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning
Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting
these data is challenging without expert input. We present Automated Tissue Alignment and …
these data is challenging without expert input. We present Automated Tissue Alignment and …