ARK: Aggregation of reads by k-means for estimation of bacterial community composition

David Koslicki, Saikat Chatterjee, Damon Shahrivar, Alan W. Walker, Suzanna C. Francis, Louise J. Fraser, Mikko Vehkaperä, Yueheng Lan, Jukka Corander

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations

    Abstract

    Motivation 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. Results 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 k-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 aggregation of reads is a pre-processing 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 k-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. Availability An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.

    Original languageEnglish (US)
    Article numbere0140644
    JournalPloS one
    Volume10
    Issue number10
    DOIs
    StatePublished - Oct 23 2015

    All Science Journal Classification (ASJC) codes

    • Biochemistry, Genetics and Molecular Biology(all)
    • Agricultural and Biological Sciences(all)
    • General

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