ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition
Identifieur interne : 001014 ( Pmc/Curation ); précédent : 001013; suivant : 001015ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition
Auteurs : David Koslicki [États-Unis] ; Saikat Chatterjee [Suède] ; Damon Shahrivar [Suède] ; Alan W. Walker [Royaume-Uni] ; Suzanna C. Francis [Royaume-Uni] ; Louise J. Fraser [Royaume-Uni] ; Mikko Vehkaper [Royaume-Uni] ; Yueheng Lan [République populaire de Chine] ; Jukka Corander [Finlande]Source :
- PLoS ONE [ 1932-6203 ] ; 2015.
Abstract
Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.
There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order
An open source, platform-independent implementation of the method in the Julia programming language is freely available at
Url:
DOI: 10.1371/journal.pone.0140644
PubMed: 26496191
PubMed Central: 4619776
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition</title>
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<front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Motivation</title>
<p>Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.</p>
</sec>
<sec id="sec002"><title>Results</title>
<p>There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order <italic>k</italic>
-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The <italic>aggregation of reads</italic>
is a <italic>pre-processing</italic>
approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of <italic>k</italic>
-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity.</p>
</sec>
<sec id="sec003"><title>Availability</title>
<p>An open source, platform-independent implementation of the method in the Julia programming language is freely available at <ext-link ext-link-type="uri" xlink:href="https://github.com/dkoslicki/ARK">https://github.com/dkoslicki/ARK</ext-link>
. A Matlab implementation is available at <ext-link ext-link-type="uri" xlink:href="http://www.ee.kth.se/ctsoftware">http://www.ee.kth.se/ctsoftware</ext-link>
.</p>
</sec>
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<publisher><publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26496191</article-id>
<article-id pub-id-type="pmc">4619776</article-id>
<article-id pub-id-type="publisher-id">PONE-D-15-15487</article-id>
<article-id pub-id-type="doi">10.1371/journal.pone.0140644</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition</article-title>
<alt-title alt-title-type="running-head">ARK</alt-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Koslicki</surname>
<given-names>David</given-names>
</name>
<xref ref-type="aff" rid="aff001"><sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chatterjee</surname>
<given-names>Saikat</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Shahrivar</surname>
<given-names>Damon</given-names>
</name>
<xref ref-type="aff" rid="aff002"><sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Walker</surname>
<given-names>Alan W.</given-names>
</name>
<xref ref-type="aff" rid="aff003"><sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Francis</surname>
<given-names>Suzanna C.</given-names>
</name>
<xref ref-type="aff" rid="aff004"><sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Fraser</surname>
<given-names>Louise J.</given-names>
</name>
<xref ref-type="aff" rid="aff005"><sup>5</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Vehkaperä</surname>
<given-names>Mikko</given-names>
</name>
<xref ref-type="aff" rid="aff006"><sup>6</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lan</surname>
<given-names>Yueheng</given-names>
</name>
<xref ref-type="aff" rid="aff007"><sup>7</sup>
</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Corander</surname>
<given-names>Jukka</given-names>
</name>
<xref ref-type="aff" rid="aff008"><sup>8</sup>
</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label>
<addr-line>Dept of Mathematics, Oregon State University, Corvallis, United States of America</addr-line>
</aff>
<aff id="aff002"><label>2</label>
<addr-line>Dept of Communication Theory, KTH Royal Institute of Technology, Stockholm, Sweden</addr-line>
</aff>
<aff id="aff003"><label>3</label>
<addr-line>Microbiology Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom</addr-line>
</aff>
<aff id="aff004"><label>4</label>
<addr-line>MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom</addr-line>
</aff>
<aff id="aff005"><label>5</label>
<addr-line>Illumina Cambridge Ltd., Chesterford Research Park, Essex, United Kingdom</addr-line>
</aff>
<aff id="aff006"><label>6</label>
<addr-line>Dept of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom</addr-line>
</aff>
<aff id="aff007"><label>7</label>
<addr-line>Dept of Physics, Tsinghua University, Beijing, China</addr-line>
</aff>
<aff id="aff008"><label>8</label>
<addr-line>Dept of Mathematics and Statistics, University of Helsinki, Helsinki, Finland</addr-line>
</aff>
<contrib-group><contrib contrib-type="editor"><name><surname>Badger</surname>
<given-names>Jonathan H.</given-names>
</name>
<role>Editor</role>
<xref ref-type="aff" rid="edit1"></xref>
</contrib>
</contrib-group>
<aff id="edit1"><addr-line>National Cancer Institute, UNITED STATES</addr-line>
</aff>
<author-notes><fn fn-type="COI-statement" id="coi001"><p><bold>Competing Interests: </bold>
L.J.F. received funding in the form of salary from Illumina Cambridge Ltd. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.</p>
</fn>
<fn fn-type="con" id="contrib001"><p>Conceived and designed the experiments: SC DK DS. Performed the experiments: DK SC DS. Analyzed the data: SC DK AWW JC. Contributed reagents/materials/analysis tools: AWW SCF LJF JC. Wrote the paper: DK SC AWW MV YL JC. Led the team: SC.</p>
</fn>
<corresp id="cor001">* E-mail: <email>sach@kth.se</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2015</year>
</pub-date>
<pub-date pub-type="epub"><day>23</day>
<month>10</month>
<year>2015</year>
</pub-date>
<volume>10</volume>
<issue>10</issue>
<elocation-id>e0140644</elocation-id>
<history><date date-type="received"><day>20</day>
<month>4</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>28</day>
<month>9</month>
<year>2015</year>
</date>
</history>
<permissions><license xlink:href="https://creativecommons.org/publicdomain/zero/1.0/"><license-p>This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:type="simple" xlink:href="pone.0140644.pdf"></self-uri>
<abstract><sec id="sec001"><title>Motivation</title>
<p>Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging.</p>
</sec>
<sec id="sec002"><title>Results</title>
<p>There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order <italic>k</italic>
-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The <italic>aggregation of reads</italic>
is a <italic>pre-processing</italic>
approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of <italic>k</italic>
-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity.</p>
</sec>
<sec id="sec003"><title>Availability</title>
<p>An open source, platform-independent implementation of the method in the Julia programming language is freely available at <ext-link ext-link-type="uri" xlink:href="https://github.com/dkoslicki/ARK">https://github.com/dkoslicki/ARK</ext-link>
. A Matlab implementation is available at <ext-link ext-link-type="uri" xlink:href="http://www.ee.kth.se/ctsoftware">http://www.ee.kth.se/ctsoftware</ext-link>
.</p>
</sec>
</abstract>
<funding-group><funding-statement>This work was supported by the Swedish Research Council Linnaeus Centre ACCESS (S.C.), ERC grant 239784 (J.C.), the Academy of Finland Center of Excellence COIN (J.C.), the Academy of Finland (M.V.), the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) (A.W.W), and the UK MRC/DFID grant G1002369 (S.C.F). L.J.F. received funding in the form of salary from Illumina Cambridge Ltd. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts><fig-count count="10"></fig-count>
<table-count count="0"></table-count>
<page-count count="16"></page-count>
</counts>
<custom-meta-group><custom-meta id="data-availability"><meta-name>Data Availability</meta-name>
<meta-value>The data and programs are available from GitHub (<ext-link ext-link-type="uri" xlink:href="https://github.com/dkoslicki/ARK">https://github.com/dkoslicki/ARK</ext-link>
) or from here (<ext-link ext-link-type="uri" xlink:href="http://www.ee.kth.se/ctsoftware">http://www.ee.kth.se/ctsoftware</ext-link>
). Further real biological data that we used for experiment have been submitted to the European Nucleotide Archive using the accession number PRJEB9828.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
<notes><title>Data Availability</title>
<p>The data and programs are available from GitHub (<ext-link ext-link-type="uri" xlink:href="https://github.com/dkoslicki/ARK">https://github.com/dkoslicki/ARK</ext-link>
) or from here (<ext-link ext-link-type="uri" xlink:href="http://www.ee.kth.se/ctsoftware">http://www.ee.kth.se/ctsoftware</ext-link>
). Further real biological data that we used for experiment have been submitted to the European Nucleotide Archive using the accession number PRJEB9828.</p>
</notes>
</front>
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