Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

• Published in: PloS one 2016-04, Vol.11 (4), p.e0154493-e0154493
• Main Authors: Tandon, Disha, Haque, Mohammed Monzoorul, Mande, Sharmila S
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Bioinformatics; Biology and Life Sciences; Classification; Communities; Computer and Information Sciences; Correlation; Data mining; Data Mining - methods; Data processing; Databases, Genetic; Datasets; Downloading; Ecological niches; Ecology and Environmental Sciences; Efficiency; Gastrointestinal Microbiome; Genomics; Humans; Intestinal microflora; Metabolism; Metagenome; Methods; Microbial activity; Microbial colonies; Microbial Interactions; Microbiomes; Microorganisms; Mining; Pattern recognition (Computers); Phylogenetics; Physical Sciences; R&D; Research & development; Research and Analysis Methods; Samples; Statistical analysis; Statistical correlation; Statistical methods; Studies; Subgroups; Taxonomy; Web Browser
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0154493

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only 'pair-wise interactions'. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the 'Apriori algorithm' for deriving 'microbial association rules' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various '(subsets of) features/items' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between 'subsets/subgroups' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving 'microbial association rules' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.