However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions.
We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts.
We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues.
In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.
The resulting machine learning task is to build a model that can correctly rank the background samples containing the stemness signal above those that do not.
The accuracy is evaluated via Area under the ROC curve (AUC), which can be interpreted as the probability that the predictor correctly ranks a mixture sample above a non-mixture sample.
1. 到底什么是one class？