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ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition

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https://ir.library.oregonstate.edu/concern/articles/ff3657098

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  • 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.
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  • Koslicki, D., Chatterjee, S., Shahrivar, D., Walker, A. W., Francis, S. C., Fraser, L. J., ... & Corander, J. (2015). ARK: Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition. PLoS ONE, 10(10), e0140644. doi:10.1371/journal.pone.0140644
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  • 10
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  • 10
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  • An open source, platform-independent implementation of the method in the Julia programming language is freely available at:
  • A Matlab implementation is available at:
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  • 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.
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file details KoslickiDavidMathARKAggregationReads.pdf 2017-07-26 Público Baixar