Cellarity releases novel, open-source, single-cell dataset
Cellarity announced the release of a unique single-cell dataset to accelerate innovation in mapping multimodal genetic information across cell states and over time. This dataset will be used to power a competition hosted by Open Problems in Single-Cell Analysis.
Cells are among the most complex and dynamic systems and are regulated by the interplay of DNA, RNA, and proteins. Recent technological advances have made it possible to measure these cellular features and such data provide, for the first time, a direct and comprehensive view spanning the layers of gene regulation that drive biological systems and give rise to disease.
To drive innovation for such data, Cellarity generated a time course profiling in vitro differentiation of blood progenitors, a dataset designed in collaboration with scientists at Yale University, Chan Zuckerberg Biohub, and Helmholtz Munich. This time course will be used to power a competition to develop algorithms that learn the underlying relationships between DNA, RNA, and protein modalities across time. Solving this open problem will help elucidate complex regulatory processes that are the foundation for cell differentiation in health and disease.
