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


BACKGROUND
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.
Dept of Mathematics, Oregon State University, Corvallis, United States of America.
David
D
Koslicki
Dept of Communication Theory, KTH Royal Institute of Technology, Stockholm, Sweden.
Saikat
S
Chatterjee
Dept of Communication Theory, KTH Royal Institute of Technology, Stockholm, Sweden.
Damon
D
Shahrivar
Microbiology Group, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, United Kingdom.
Alan W
AW
Walker
MRC Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Suzanna C
SC
Francis
Illumina Cambridge Ltd., Chesterford Research Park, Essex, United Kingdom.
Louise J
LJ
Fraser
Dept of Electronic and Electrical Engineering, University of Sheffield, Sheffield, United Kingdom.
Mikko
M
Vehkaperä
Dept of Physics, Tsinghua University, Beijing, China.
Yueheng
Y
Lan
Dept of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Jukka
J
Corander
D000465:Algorithms; D001419:Bacteria / Q000145:classification / Q000235:genetics*; D016000:Cluster Analysis; D004269:DNA, Bacterial / Q000737:chemistry / Q000235:genetics; D005243:Feces / Q000382:microbiology; D006801:Humans; D020407:Internet; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota / Q000235:genetics*; D016133:Polymerase Chain Reaction; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA
D004269:DNA, Bacterial; D012336:RNA, Ribosomal, 16S