FastSpar: rapid and scalable correlation estimation for compositional data.
Oxford University Press (OUP)
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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
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.
Algorithms, Software, Microbiota
External DOI: https://doi.org/10.1093/bioinformatics/bty734
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285621
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/