Dataset Information
Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.
Ontology highlight
ABSTRACT: Motivation
Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota.Results
By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data.Availability
The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/.Contact
zliu@umm.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Liu Z
PROVIDER: S-EPMC3223360 | biostudies-literature | 2011 Dec
REPOSITORIES: biostudies-literature
Publications
Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data.
Bioinformatics (Oxford, England) 20111007 23
<h4>Motivation</h4>Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicat ...[more]