Structure-preserving visualisation of high dimensional single-cell datasets
Abstract
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...
Paper Details
Title
Structure-preserving visualisation of high dimensional single-cell datasets
Published Date
Jun 20, 2019
Journal
Volume
9
Issue
1
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