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


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


BACKGROUND
Supplementary data are available at Bioinformatics online.


BACKGROUND
jinchen@msu.edu, kramerd8@cns.msu.edu or rongjin@cse.msu.edu.
Department of Computer Science and Engineering.
Qiaozi
Q
Gao
Department of Energy Plant Research Laboratory and.
Elisabeth
E
Ostendorf
Department of Energy Plant Research Laboratory and.
Jeffrey A
JA
Cruz
Department of Computer Science and Engineering.
Rong
R
Jin
Department of Energy Plant Research Laboratory and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
David M
DM
Kramer
Department of Computer Science and Engineering, Department of Energy Plant Research Laboratory and.
Jin
J
Chen
D000465:Algorithms; D017360:Arabidopsis / Q000502:physiology*; D002736:Chloroplasts / Q000235:genetics; D016000:Cluster Analysis; D005838:Genotype; D016013:Likelihood Functions; D009154:Mutation / Q000235:genetics; D010641:Phenotype; D010788:Photosynthesis; D015203:Reproducibility of Results; D013223:Statistics as Topic; D018709:Statistics, Nonparametric