GCoda: Conditional Dependence Network Inference for Compositional Data.

ABSTRACT: The increasing quality and the reducing cost of high-throughput sequencing technologies for 16S rRNA gene profiling enable researchers to directly analyze microbe communities in natural environments. The direct interactions among microbial species of a given ecological system can help us understand the principles of community assembly and maintenance under various conditions. Compositionality and dimensionality of microbiome data are two main challenges for inferring the direct interaction network of microbes. In this article, we use the logistic normal distribution to model the background mechanism of microbiome data, which can appropriately deal with the compositional nature of the data. The direct interaction relationships are then modeled via the conditional dependence network under th

SUBMITTER: Fang H 

PROVIDER: S-EPMC5510714 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature