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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 on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space, adds new data points to existing embeddings using a parametric mapping function, and scales linearly to hundreds of thousands of cells. ivis is made publicly available through Python and R interfaces on https://github.com/beringresearch/ivis .

SUBMITTER: Szubert B

PROVIDER: S-EPMC6586841 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Structure-preserving visualisation of high dimensional single-cell datasets.

Szubert Benjamin B Cole Jennifer E JE Monaco Claudia C Drozdov Ignat I

Scientific reports 20190620 1

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 ...[more]

PMID: 31222035

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