TY - JOUR T1 - FastSpar: Rapid and scalable correlation estimation for compositional data JF - bioRxiv DO - 10.1101/272583 SP - 272583 AU - Stephen C. Watts AU - Scott C. Ritchie AU - Michael Inouye AU - Kathryn E. Holt Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/03/23/272583.abstract N2 - 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 specialised 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 parallelisable 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. FastSpar source code, precompiled binaries, and platform packages are freely available on GitHub: github.com/scwatts/FastSpar ER -