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Bioinformatics. 2016 Jan 1;32(1):67-76. doi: 10.1093/bioinformatics/btv515. Epub 2015 Sep 3.

Inter-functional analysis of high-throughput phenotype data by non-parametric clustering and its application to photosynthesis.

Author information

1
Department of Computer Science and Engineering.
2
Department of Energy Plant Research Laboratory and.
3
Department of Energy Plant Research Laboratory and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
4
Department of Computer Science and Engineering, Department of Energy Plant Research Laboratory and.

Abstract

MOTIVATION:

Phenomics is the study of the properties and behaviors of organisms (i.e. their phenotypes) on a high-throughput scale. New computational tools are needed to analyze complex phenomics data, which consists of multiple traits/behaviors that interact with each other and are dependent on external factors, such as genotype and environmental conditions, in a way that has not been well studied.

RESULTS:

We deployed an efficient framework for partitioning complex and high dimensional phenotype data into distinct functional groups. To achieve this, we represented measured phenotype data from each genotype as a cloud-of-points, and developed a novel non-parametric clustering algorithm to cluster all the genotypes. When compared with conventional clustering approaches, the new method is advantageous in that it makes no assumption about the parametric form of the underlying data distribution and is thus particularly suitable for phenotype data analysis. We demonstrated the utility of the new clustering technique by distinguishing novel phenotypic patterns in both synthetic data and a high-throughput plant photosynthetic phenotype dataset. We biologically verified the clustering results using four Arabidopsis chloroplast mutant lines.

AVAILABILITY AND IMPLEMENTATION:

Software is available at www.msu.edu/~jinchen/NPM.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

CONTACT:

jinchen@msu.edu, kramerd8@cns.msu.edu or rongjin@cse.msu.edu.

PMID:
26342101
DOI:
10.1093/bioinformatics/btv515
[Indexed for MEDLINE]

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