RT Journal Article
SR Electronic
T1 Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 093898
DO 10.1101/093898
A1 Sawsan Kanj
A1 Thomas Brüls
A1 Stéphane Gazut
YR 2016
UL http://biorxiv.org/content/early/2016/12/15/093898.abstract
AB We present a new algorithm to cluster high dimensional sequence data, and its application to the field of metagenomics, which aims to reconstruct individual genomes from a mixture of genomes sampled from an environ-mental site, without any prior knowledge of reference data (genomes) or the shape of clusters. Such problems typically cannot be solved directly with classical approaches seeking to estimate the density of clusters, e.g., using the shared nearest neighbors rule, due to the prohibitive size of contemporary sequence datasets. We explore here a new method based on combining the shared nearest neighbor (SNN) rule with the concept of Locality Sensitive Hashing (LSH). The proposed method, called LSH-SNN, works by randomly splitting the input data into smaller-sized subsets (buckets) and, employing the shared nearest neighbor rule on each of these buckets. Links can be created among neighbors sharing a sufficient number of elements, hence allowing clusters to be grown from linked elements. LSH-SNN can scale up to larger datasets consisting of millions of sequences, while achieving high accuracy across a variety of sample sizes and complexities.