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Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reduction of single-cell expression data. ivis utilizes a siamese neural network architecture that is trained using a novel triplet loss function. Results on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space, learns a parametric mapping function that naturally adds new data points to existing embeddings, and scales linearly to hundreds of thousands of cells. ivis is made publicly available through Python and R interfaces on


Journal article


Scientific Reports


Nature Research (part of Springer Nature)

Publication Date