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

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome
from single cells is transforming our understanding of cell biology in health and disease. In …

The natural history of cancers can be understood through the lens of evolution given that the
driving forces of cancer development are mutation and selection of fitter clones. Cancer …

The relationship between the human placenta—the extraembryonic organ made by the
fetus, and the decidua—the mucosal layer of the uterus, is essential to nurture and protect …

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 …

The development of single-cell multimodal assays provides a powerful tool for investigating
multiple dimensions of cellular heterogeneity, enabling new insights into development …

Legumes form symbiosis with rhizobium leading to the development of nitrogen-fixing
nodules. By integrating single-nucleus and spatial transcriptomics, we established a cell …

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 …

Unsupervised machine learning methods are important analytical tools that can facilitate the
analysis and interpretation of high-dimensional data. Unsupervised machine learning …

Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count
data because, in contrast to real-valued alternatives such as factor analysis, it produces an …