Eirini's paper accepted in Nature Communications
Sensitive detection of rare disease-associated cell subsets via representation learning. Using CellCnn, we identify paracrine signaling-, AIDS onset- and rare CMV infection-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%.
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. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signaling-, AIDS onset- and rare CMV infection-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%.