HuBMAP MCs Demo Tools to Place Scientists’ Data into Genomic or Spatial Contexts, Leverage Visible Human Database to Learn
March 8, 2021
This HuBMAP SciTech Webinar featured two Mapping Components (MCs) that have created online portals allowing scientists to enter their own data in a context that allows comparison and analysis with their MCs’ data sets. These two MCs’ efforts focus on transcriptomic context for single-cell data and spatial context for tissue images, respectively, with the latter also offering a massive, open online course based on the Visible Human database. A recording of the presentations can be found here.
Azimuth DemoRahul Satija of the New York Genome Center demonstrated Azimuth, a web-based tool that allows researchers to rapidly enable mapping and annotation single-cell sequencing profiles for as many as 100,000 cells or more. The approach, he explained, is inspired by a similar paradigm from the human genome project: Initial assembly and building a genome map is time- and effort-intensive, requiring high-quality data. However once that is accomplished, mapping new data to the reference genome is relatively fast and easy. Given the reference maps that are generated for Azimuth by Satija’s HuBMAP MC, accurate and robust comparisons and annotations of single-cell expression patterns can be efficiently accomplished. Satija demonstrated how a database of a few thousand cells can be mapped into Azimuth in less than a minute—mapping of 100,000 cells or more takes seven to eight minutes, he added. Once entered, the data from each cell is projected into the reference map, receiving an annotation based on that reference as well as a prediction score to assign the level of conference for that mapping. A graphical display allows identification of cell type at a hierarchical level chosen by the researcher, matching the scientist’s data for a given cell to the most similar reference cell type in terms of gene activity as reflected in which kinds of RNA transcripts have been copied from the DNA. The system automatically computes the best markers for a given cell type and allows for comparison of cell types across species and disease states. Metadata such as gender, patient COVID status and spatial location are supported, and scientists can download anything from reference-map coordinates to individual cell annotations to further analyze on their local computer. Azimuth can be accessed here.