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

Gao, Qiaozi; Ostendorf, Elisabeth; Cruz, Jeffrey A.; Jin, Rong; Kramer, David; Chen, Jin

doi:10.1093/bioinformatics/btv515

Cited by 5 publications

(7 citation statements)

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“…A computer vision-based tracking approach to organ development revealed temperature-compensated cell production rates and elongation zone lengths in roots through comparative image analysis of wild-type Arabidopsis and a phytochrome-interacting factor 4- and 5 - double mutant of Arabidopsis [94]. A new clustering technique, nonparametric modeling, was applied to a high-throughput photosynthetic phenotype dataset and showed efficiency for discriminating Arabidopsis chloroplast mutant lines [95]. In rice, a large-scale T-DNA insertional mutant resource was developed and applied to phenotyping 68 traits belonging to 11 categories and 3 quantitative traits, screened by well-trained breeders under field conditions [96].…”

Section: Reviewmentioning

confidence: 99%

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

et al. 2018

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Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.

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Section: Reviewmentioning

confidence: 99%

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

et al. 2018

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“…We adopt a meta-clustering approach to identify the relationships among all the tested genotypes in each time frame T i [5], [18]. In the meta-clustering process, we repeatedly cluster the phenotype values of all the genotypes P in T i using non-parametric clustering with random anchor points [12]. The center of non-parametric clustering is a cloudof-points representation.…”

Section: A Tep-finder Phase 1 Identifying Seed Phenomenonmentioning

confidence: 99%

“…where Pr(D ij ) is given in Equation 1, and n is the total number of genotypes in M. This optimization problem can be effectively solved by employing NPM, a non-parametric clustering method for phenomics data analysis [12]. Based on the Nadaraya-Watson method for kernel density estimation [20], [24], [27] and following the framework of maximum likelihood estimation, NPM uses optimal density functions and applies a nonparametric clustering technique to group genotypes into the same cluster if their clouds-of-points share similar arbitrary shapes.…”

Section: A Tep-finder Phase 1 Identifying Seed Phenomenonmentioning

confidence: 99%

“…To our knowledge, there is no existing algorithm exactly designed for emerging phenomena identification. Tools, such as DHAC and NPM [12], [19], may be slightly modified to achieve the goal. Here we discuss two existing approaches with additional steps adopted for emerging phenomenon discovery.…”

Section: Introductionmentioning

confidence: 99%

“…NPM is a non-parametric clustering method that can simultaneously cluster subjects with arbitrary cluster shapes [12]. NPM represents the phenotypes of each genotype in a serial of continuous time points as a cloud of points.…”

Section: Introductionmentioning

confidence: 99%

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Identifying emerging phenomenon in long temporal phenotyping experiments

Peng

Hoh

et al. 2019

Bioinformatics

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Motivation The rapid improvement of phenotyping capability, accuracy and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. Results We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust and biologically significant. Availability and implementation The source code, manual and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/. Supplementary information Supplementary data are available at Bioinformatics online.

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PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data

Yang¹,

Feng

et al. 2017

Bioinformatics

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

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

Cited by 5 publications

References 62 publications

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective

Identifying emerging phenomenon in long temporal phenotyping experiments

PhenoCurve: capturing dynamic phenotype-environment relationships using phenomics data

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