Sensitive detection of rare disease-associated cell subsets via representation learning
By Eirini Arvaniti, Manfred Claassen. 2016
170 citations recorded by the source corpus.
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 signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. While rare cell subpopulations frequently make the difference between health and disease, their detection remains a challenge. Here, the authors devise CellCnn, a representation learning approach to detecting such rare cell populations from high-dimensional single cell data, and, among other examples, demonstrate its capacity for detecting rare leukaemic blasts in minimal residual disease.
Subfields: Biophysics, Molecular Biology
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Citations in the corpus
- Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, background): “Future developments in this field will likely borrow from the mass cytometry (e.g. Tibshirani et al , 2002; Arvaniti & Claassen, 2017; Lun et al , 2017; Weber et al , 2018) or the microbiome literature (Gloor et al , 2017), where compositional data analysis has received more attention.”