dc.contributor.author | John, C | |
dc.contributor.author | Watson, D | |
dc.contributor.author | Barnes, M | |
dc.contributor.author | Pitzalis, C | |
dc.contributor.author | Lewis, M | |
dc.date.accessioned | 2020-07-30T11:18:00Z | |
dc.date.available | 2020-07-30T11:18:00Z | |
dc.date.issued | 2019-05-13 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/65942 | |
dc.description.abstract | 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. Contact: christopher.john@qmul.ac.uk | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | Spectrum: Fast density-aware spectral clustering for single and multi-omic data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1101/636639 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
dc.rights.license | CC BY NC ND | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by-nd/4.0/ | en_US |