Recent advances in single-cell technologies have enabled high-throughput molecular
profiling of cells across modalities and locations. Single-cell transcriptomics data can now …

Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Molecular characterization of cell types using single-cell transcriptome sequencing is
revolutionizing cell biology and enabling new insights into the physiology of human organs …

Despite their crucial role in health and disease, our knowledge of immune cells within
human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 …

Map** single-cell sequencing profiles to comprehensive reference datasets provides a
powerful alternative to unsupervised analysis. However, most reference datasets are …

Generative pretrained models have achieved remarkable success in various domains such
as language and computer vision. Specifically, the combination of large-scale diverse …

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep
biological understanding requires more than a taxonomic listing of clusters. As new methods …

The liver is the largest solid organ in the body, yet it remains incompletely characterized.
Here we present a spatial proteogenomic atlas of the healthy and obese human and murine …

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to
technical factors, including the number of molecules detected in each cell, which can …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …