Open AccessGrammR: graphical representation and modeling of count data with application in metagenomics
Author(s) -
Deepak Nag Ayyala,
Shili Lin
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv032
Subject(s) - multidimensional scaling , representation (politics) , metagenomics , unifrac , cluster analysis , phylogenetic tree , computer science , metric (unit) , distance matrices in phylogeny , tree (set theory) , measure (data warehouse) , graphical model , principal component analysis , data mining , artificial intelligence , pattern recognition (psychology) , mathematics , machine learning , biology , combinatorics , biochemistry , operations management , genetics , 16s ribosomal rna , politics , political science , bacteria , law , economics , gene
Microbiota compositions have great implications in human health, such as obesity and other conditions. As such, it is of great importance to cluster samples or taxa to visualize and discover community substructures. Graphical representation of metagenomic count data relies on two aspects, measure of dissimilarity between samples/taxa and algorithm used to estimate coordinates to study microbiota communities. UniFrac is a dissimilarity measure commonly used in metagenomic research, but it requires a phylogenetic tree. Principal coordinate analysis (PCoA) is a popular algorithm for estimating two-dimensional (2D) coordinates for graphical representation, although alternative and higher-dimensional representations may reveal underlying community substructures invisible in 2D representations.
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