Technological advances enabling massively parallel measurement of biological features—
such as microarrays, high-throughput sequencing and mass spectrometry—have ushered in …

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue
homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient …

Spatially resolved transcriptomics has been dramatically transforming biological and
medical research in various fields. It enables transcriptome profiling at single‐cell, multi …

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to
anatomical context. SRT approaches that use next-generation sequencing (NGS) combine …

Methods for in silico deconvolution of bulk transcriptomics can characterize the cellular
composition of the tumor microenvironment, quantifying the abundance of cell types …

Motivation Single-cell omics technologies have enabled the quantification of molecular
profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving …

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell
resolution where each captured location (spot) may contain a mixture of cells from …

Spatial transcriptomics, leveraging sequencing-and imaging-based techniques, has
emerged as a groundbreaking technology for mapping gene expression within the complex …

A major challenge in sequencing-based spatial transcriptomics (ST) is resolution limitations.
Tissue sections are divided into hundreds of thousands of spots, where each spot invariably …

Tumour heterogeneity significantly affects cancer progression and therapeutic response, yet
quantifying it from bulk molecular data remains challenging. Deconvolution algorithms …