Spectrum: Fast density-aware spectral clustering for single and multi-omic data

John, C. R.; Watson, D.; Barnes, M.; Pitzalis, C.; Lewis, M.

doi:10.1101/636639

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bioRxiv · 10.1101/636639

Spectrum: Fast density-aware spectral clustering for single and multi-omic data

John, C. R.·Watson, D.·Barnes, M.·Pitzalis, C.·Lewis, M.

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Abstract

Clustering of single or multi-omic data is key to developing personalised medicine and identifying new cell types. We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data. The method uses a new density-aware kernel that adapts to data scale and density. It uses a tensor product graph data integration and diffusion technique to reveal underlying structures and reduce noise. We developed a powerful method of eigenvector analysis to determine the number of clusters. Benchmarking Spectrum on 21 datasets demonstrated improvements in runtime and performance relative to other state-of-the-art methods.\n\nContact: christopher.john@qmul.ac.uk

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John, C. R., Watson, D., Barnes, M., Pitzalis, C., Lewis, M.. 2019-05-13. Spectrum: Fast density-aware spectral clustering for single and multi-omic data. https://doi.org/10.1101/636639

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