Helping Scientists Explore Single-Cell Datasets with High-Quality Visualizations

Apr 28, 2020

Brushes of rainbow color in shades of blue, yellow and purple radiate outward from a white background, representing tissues colored by category across cells.
A visualization in cellxgene with coloring by tissue-type category across cells.

Information gathered from biomedical research can include terabytes of data that are difficult and time-consuming to analyze. Cellxgene is a tool that allows scientists to explore and visualize high dimensional single-cell datasets in an interactive way, allowing them to surface important information faster that could lead to discoveries in treating disease.

This easy-to-use, open source tool integrates with existing tools like SciPy and Jupyter. Scientists can visualize gene expression at the level of an entire dataset or a particular subset of cells to help identify cell types, which can in turn help identify diseases. Cellxgene allows researchers to keep track of and annotate cell types as they identify them in their dataset. The tool also enables plotting one gene’s expression level against another gene to compare how these different genes are expressed across a dataset.

Researchers can view metadata, such as which cells came from a certain tissue type, and then color code these attributes to display more context about the cells they view. Computational biologists and experimental scientists use cellxgene to explore information from the global Human Cell Atlas project, like a single-cell atlas of the human lung that is helping researchers understand COVID-19 transmission. Many institutions have set up their own public sites featuring their data within cellxgene.


Visit

cellxgene.cziscience.com

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