CYTO Virtual Interactive 2021 Plenary - Data Science and Cytometry: Why Taking It “One Cell at a Time” May Not Be Enough…
The root of the word “Cytometry” is two Greek words “Kytos” and “metria”, literally translating as “cell measurement”. As cytometrists, we seek to use technologies and methodologies that allow us to turn our cells in to sets of numbers that hopefully then allow us to accurately describe the biology under investigation. One of the biggest challenges we face is how to deal with such rich data, often composed of 100,000s of single cells and possibly 1000s of individual measurement per cell. Moreover, we are now in the realms of “multi-omics” cytometry approaches as well as technologies that are able to conduct tissue-based spatial analysis adding further layers of complexity. Global initiatives such as the Human Cell Atlas (HCA) and the Chan Zuckerberg Initiative (CZI) are being driven at pace by single cell (cytometry) technologies and are all too aware of the challenges posed by data analysis. Our single cell data however is often part of a much bigger picture where it must be integrated an analyzed within the wider context of social, occupational and environmental metrics as well as those of a clinical, biological and behavioral nature. This session at CYTO Virtual Interactive 2021 will focus on enabling and empowering tools for single cell data analysis and we will hear from two leading experts in this field. Please join us for what will be a superb event.
Nima Aghaeepour, PhD
Nima Aghaeepour is an assistant professor at Stanford University. He performed his graduate studies in the University of British Columbia with Ryan Brinkman and Holger Hoos, in collaboration with Mario Roederer and Pratip Chattopadhyay at the National Institutes of Health, followed by a postdoctoral fellowship with Garry Nolan at Stanford University. His laboratory develops machine learning/artificial intelligence methods to study the immune system in clinical settings. His interest focus on the intersection of data sciences, immunology, and clinical phenotyping. His research includes integrative “multiomics” analysis across cytomics, genomics, and proteomics assays, as well as quantitative clinical phenotyping using wearable devices, to produce a holistic understanding of immunity.
Sofie Van Gassen, PhD
Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research
Sofie Van Gassen received her PhD in computer science engineering from Ghent University in 2017. During her PhD she developed machine learning techniques for flow cytometry data, including the FlowSOM algorithm. She is now working on tools for computational cytometry in clinical studies during her postdoc in the Saeys lab (VIB-UGent Center for Inflammation Research). Since 2018, she is an FWO postdoctoral fellow and an ISAC Ingram Marylou Scholar
CMLE Credit: 1.0