Fig. 1

Overview. Schematic overview of CytoCoSet. a Given a multi-sample single-cell dataset additional covariates measured in each sample, b the CytoCoSet algorithm defines a set of triplets based on Random Fourier Features (RFFs) to constrain the process of learning per-sample embedding vectors. A triplet is a combination of three samples, such that two samples have similar covariates and should have similar embeddings, and the third sample is distinct in terms of covariates and should therefore have a more divergent embedding. c The loss function specified to optimize the embeddings is comprised of a binary cross entropy term to enforce prediction accuracy, and a triplet term, which encodes covariate-based similarity constraints. d Embedding vectors learned by the model can be used to train machine learning models of clinical outcome