PT  - JOURNAL ARTICLE
AU  - Stephen C. Watts
AU  - Scott C. Ritchie
AU  - Michael Inouye
AU  - Kathryn E. Holt
TI  - FastSpar: Rapid and scalable correlation estimation for compositional data
AID  - 10.1101/272583
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 272583
4099  - http://biorxiv.org/content/early/2018/03/23/272583.short
4100  - http://biorxiv.org/content/early/2018/03/23/272583.full
AB  - 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