Random projection based clustering for population genomics

Sotiris Tasoulis, Lu Cheng, Niko Valimaki, Nicholas J. Croucher, Simon R. Harris, William P. Hanage, Teemu Roos, Jukka Corander

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

8 Citations (Scopus)

Abstract

Recent data revolution in population genomics for bacteria has increased the size of aligned sequence data sets by two-to-three orders of magnitude. This trend is expected to continue in the near future, putting an emphasis on applicability of big data techniques to leverage biologically important insights. Moreover, with the increasing density of sampling, it may also be necessary to consider alignment-free sequence analysis techniques combined with clustering to yield a sufficient insight to data. This leads to ultra high-dimensional data with tens of millions of variables, which can no longer be handled by the existing population genomic methods. Using the largest bacterial sequence data sets published to date, we demonstrate that random projection based clustering provides a highly accurate and several orders of magnitude faster approach to the analysis of both alignment-based and alignment-free genome data sets, compared with the Bayesian model-based analysis that is currently considered as the state-of-the-art. Hence, clustering methods for big data harbor considerable potential for important applications in genomics and could pave way for novel analysis pipelines even in the online setting when executed in a massively parallel computing environment.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
PublisherIEEE
Pages675-682
Number of pages8
ISBN (Electronic)9781479956654
DOIs
Publication statusPublished - 7 Jan 2015
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Big Data - Washington, United States
Duration: 27 Oct 201430 Oct 2014
Conference number: 2

Conference

ConferenceIEEE International Conference on Big Data
Abbreviated titleBig Data
CountryUnited States
CityWashington
Period27/10/201430/10/2014

Keywords

  • Clustering
  • High Dimensionality
  • Population Genomics
  • Random Projection

Fingerprint

Dive into the research topics of 'Random projection based clustering for population genomics'. Together they form a unique fingerprint.

Cite this