CCLasso: correlation inference for compositional data through Lasso.
Fang, Huaying 1,2,3; Huang, Chengcheng 4; Zhao, Hongyu 5; Deng, Minghua 1,3,6,*
[Article] Bioinformatics. 31(19):3172-3180, October 01, 2015.

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Motivation: Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis treating the observed data as absolute abundances of the microbes may lead to spurious results because the data only represent relative abundances. Special care and appropriate methods are required prior to correlation analysis for these compositional data.

Results:

Availability and implementation: CCLasso is open source and freely available from https://github.com/huayingfang/CCLasso under GNU LGPL v3.

Contact: [email protected]

Supplementary information: Supplementary data are available at Bioinformatics online.

(C) Copyright Oxford University Press 2015.