FastSpar: Rapid and scalable correlation estimation for compositional data

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

Research output: Contribution to journalArticleOtherpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1064-1066
Number of pages3
Issue number6
Publication statusPublished - 15 Mar 2019
Externally publishedYes

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