Journal article

FastSpar: rapid and scalable correlation estimation for compositional data

Stephen C Watts, Scott C Ritchie, Michael Inouye, Kathryn E Holt



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..

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University of Melbourne Researchers


Awarded by National Health and Medical Research Council of Australia

Funding Acknowledgements

This work was supported by the National Health and Medical Research Council of Australia (Project #1062227, Fellowship #1061409 to K.E.H., Fellowship #1061435 to M.I. co-funded by the Australian Heart Foundation) and by the Australian Government Research Training Program (Scholarship to S.W and S.R.).