Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but
does not capture their spatial distribution nor reveal local networks of intercellular …

White adipose tissue, once regarded as morphologically and functionally bland, is now
recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of …

Many spatially resolved transcriptomic technologies do not have single-cell resolution but
measure the average gene expression for each spot from a mixture of cells of potentially …

Inferring single-cell compositions and their contributions to global gene expression changes
from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we …

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed
by tumor cells for successful development and metastasis of tumor. With the advent of 3D …

Genetic risk variants that have been identified in genome-wide association studies of
complex diseases are primarily non-coding. Translating these risk variants into mechanistic …

Mapping cell types across a tissue is a central concern of spatial biology, but cell type
abundance is difficult to extract from spatial gene expression data. We introduce …

Many computational methods have been developed to infer cell type proportions from bulk
transcriptomics data. However, an evaluation of the impact of data transformation, pre …

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers
characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs …