Exploring High-Dimensional Biological Data with Clustergrammer2
Visualizing biological data can be challenging. Heat maps are useful for showing the basic distribution of gene expression but interactivity is needed for additional, in-depth analysis. Clustergrammer2 is a visualization tool that allows users to perform more refined clustering on cell lines and genes.
In this webinar, Dr. Nicolas Fernandez, who worked on the original Clustergrammer and developed Clustergrammer2, will demonstrate how to use the latest version of this tool. Clustergrammer2, which is now available on Jupyter Notebook, can be used to explore and analyze various types of high-dimensional biological data (e.g., single-cell gene expression data) and share those results with colleagues.
Dr. Nicolas Fernandez is a senior computational biologist at Vizgen. He developed Clustergrammer2 while at the Human Immune Monitoring Center at Mount Sinai as a computational scientist. The original Clustergrammer was developed while Dr. Fernandez was a post-doctoral fellow at the Ma'ayan Laboratory at Mount Sinai.
Upcoming Events
- Finding Neighborhoods in the Land of Spatial OmicsApril 23, 2024Data Quality for LLMs: Building a Reliable Data FoundationApril 24, 2024Cancer Research Data Exchange SummitMay 08, 2024 - May 09, 2024AI in Personalized ImmunotherapiesMay 15, 2024