FastSpar: rapid and scalable correlation estimation for compositional data.
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Publication Date
2019-03-15Journal Title
Bioinformatics
ISSN
1367-4803
Publisher
Oxford University Press (OUP)
Volume
35
Issue
6
Pages
1064-1066
Language
eng
Type
Article
Physical Medium
Print
Metadata
Show full item recordCitation
Watts, S. C., Ritchie, S., Inouye, M., & Holt, K. E. (2019). FastSpar: rapid and scalable correlation estimation for compositional data.. Bioinformatics, 35 (6), 1064-1066. https://doi.org/10.1093/bioinformatics/bty734
Abstract
SUMMARY: A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialized statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelizable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates P-values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2-3 orders of magnitude compared to SparCC. AVAILABILITY AND IMPLEMENTATION: FastSpar source code, precompiled binaries and platform packages are freely available on GitHub: github.com/scwatts/FastSpar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Keywords
Algorithms, Software, Microbiota
Identifiers
External DOI: https://doi.org/10.1093/bioinformatics/bty734
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285621
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