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The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

ABSTRACT: Oligotyping is a novel, supervised computational method that classifies closely related sequences into "oligotypes" (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

SUBMITTER: Ramette A

PROVIDER: S-EPMC4231947 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMany methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD). Although singular vectors associated with the largest singular values have strong optimality properties and can often be quite useful as a tool to summarize the data, they are linear combinations of up to all of the data points, and thus it is typically quite hard to interpret those vectors in terms of the application domain from which the data are drawn. Recently, an alternative dimensionality reduction paradigm, CUR matrix decompositions, has been proposed to address this problem and has been applied to genetic and internet data. CUR decompositions are low-rank matrix decompositions that are explicitly expressed in terms of a small number of actual columns and/or actual rows of the data matrix. Since they are constructed from actual data elements, CUR decompositions are interpretable by practitioners of the field from which the data are drawn.ResultsWe present an implementation to perform CUR matrix decompositions, in the form of a freely available, open source R-package called rCUR. This package will help users to perform CUR-based analysis on large-scale data, such as those obtained from different high-throughput technologies, in an interactive and exploratory manner. We show two examples that illustrate how CUR-based techniques make it possible to reduce significantly the number of probes, while at the same time maintaining major trends in data and keeping the same classification accuracy.ConclusionsThe package rCUR provides functions for the users to perform CUR-based matrix decompositions in the R environment. In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores. These scores, which have been used historically in diagnostic regression analysis to identify outliers, can be used by rCUR to identify the most informative data points with respect to which to express the remaining data points.

Dataset Information

The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

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