Major Category: Technologies and Bioinformatics
By Martin Wasser (Team Lead),
Yeo Joo Guan
Mass cytometry (CyTOF) measures the expression of multiple proteins in single cells (currently up to 40) and is used to characterise cellular diversity of the immune system in peripheral blood and other tissues. TII’s immune atlas is a growing database of CyTOF data acquired from blood of clinically and demographically diverse human subjects. Data of samples labeled with identical antibody panels are grouped and jointly clustered to identify distinct populations of immune cells. After determining their expression patterns, we assign cell types to these clusters. To analyse these complex multi-dimensional data, we developed a web application using the Shiny R programming environment that has two main objectives. First, users can explore the immune landscape at different levels of detail using a variety of interactive visualisation methods, such as bar charts, tSNE or UMAP scatter plots, heat maps or histograms. For instance, the abundance of immune cell types can be compared between different age groups or between patients suffering suffering from immune-diseases and healthy subjects. Second, users can upload their own cytometry data and compare them with the immune atlas. For instance, machine learning is applied to classify expression patterns in uploaded data and provide estimates about the abundance of selected immune cell populations.
Screenshot of the Immune Atlas Web application
(Click image to enlarge)
The application was implemented in Shiny R and runs in all web browsers. The bar chart (top right) compares the abundance of immune cell population between atlas (blue) and uploaded data. The heat maps at the bottom compare the expression patterns of 37 genes in 100 clusters between atlas (left) and uploaded data (right).
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