FastSpar：用于成分数据的快速可扩展相关估计。
FastSpar: rapid and scalable correlation estimation for compositional data.
机构信息
Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Australia.
Systems Genomics Lab, Baker Heart & Diabetes Institute, Melbourne, Australia.
出版信息
Bioinformatics. 2019 Mar 15;35(6):1064-1066. doi: 10.1093/bioinformatics/bty734.
SUMMARY
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.
AVAILABILITY AND IMPLEMENTATION
FastSpar source code, precompiled binaries and platform packages are freely available on GitHub: github.com/scwatts/FastSpar.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
微生物组研究的一个共同目标是利用从序列数据中提取的分类单元计数来阐明群落组成和成员相互作用。从稀疏和组成数据中推断相互作用网络需要专门的统计方法。一种流行的解决方案是 SparCC，但是它的性能限制了非常高维数据集的相互作用网络的计算。在这里，我们介绍了 FastSpar，这是 SparCC 算法的一种高效且可并行化的实现，它可以快速推断相关网络，并使用无偏估计计算 P 值。我们进一步证明，FastSpar 与 SparCC 相比，网络推断的wall time 减少了 2-3 个数量级。
可及性和实现
FastSpar 的源代码、预编译二进制文件和平台包可在 GitHub 上免费获得：github.com/scwatts/FastSpar。
补充信息
补充数据可在生物信息学在线获得。
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