(Un-)supervised learning of cell population structure from single-cell snapshot data
Thursday 4 April 2019 (12h15 - 13h00) - Génopode - Auditoire A
Abstract of the talk :
Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. I will present our representation learning approaches to detect rare cell subsets associated with disease using high-dimensional single-cell measurements and demonstrate identification of rare CMV infection and multiple sclerosis-associated cell subsets in peripheral blood, and extremely rare leukemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%, as well as identification of morphological patterns associated with cancer severity.
To meet with Dr. Claassen during the day, please register for the Doodle