RT Journal Article
SR Electronic
T1 FastSpar: Rapid and scalable correlation estimation for compositional data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 272583
DO 10.1101/272583
A1 Stephen C. Watts
A1 Scott C. Ritchie
A1 Michael Inouye
A1 Kathryn E. Holt
YR 2018
UL http://biorxiv.org/content/early/2018/03/03/272583.abstract
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