Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

Single-cell sequencing is useful for a number of applications. For example, it can reveal cellular heterogeneity inside complex tissues, such as tumors. It can also provide information about how ...

In the past decade there has been significant interest in studying the expression of our genetic code down to the level of single cells, to identify the functions and activities of any cell through ...

A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

Proceeds will help enhance workflows and expand single-cell functional assay portfolio Brad Crutchfield joins as advisor to support early access growth and upcoming global launch Lightcast, a ...

Celldetective is an open-source software integrating segmentation, tracking, and event detection to perform high-throughput end-to-end study of dynamic cell interactions, without requiring coding ...

Researchers headed by a team at the University of California, Irvine, Joe C. Wen School of Population & Public Health have built what they suggest is the first cell type-specific gene regulatory ...

Compare deep learning cell segmentation tools Cellpose and StarDist: how each works, how they differ by imaging type, and ...

Next-generation instruments improve rare-cell detection, sterility control, biosafety, and throughput for translational ...