Statistical Methods in Topological Data Analysis for Complex, High-Dimensional Data

Medina, Patrick S; Doerge, R. W.

doi:10.4148/2475-7772.1130

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…A thorough introduction to TDA can be found in [4] and in the review papers [3,5]. A conceptual introduction to TDA may be found in [6].…”

Section: Introductionmentioning

confidence: 99%

Topological Methods for the Quantification and Analysis of Complex Phenotypes

Medina¹,

Doerge²

2016

Conference on Applied Statistics in Agriculture

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Quantitative Trait Locus (QTL) mapping of complex traits, such as leaf venation or root structures, require the phenotyping and genotyping of large populations. Sufficient genotyping is accomplished with cost effective highthroughput assays, however labor costs often makes sufficient phenotyping prohibitively limited. In order to develop efficient high-throughput phenotyping platforms for complex traits algorithms and methods for quantifying these traits are needed. It is often desirable to study the spatial organization of these phenotypes from the images generated by high-throughput platforms. With the goal of quantifying the traits, many approaches try to identify several core traits useful in describing the phenotypic morphology. This simplification may lose important information about the phenotype. Rather than reducing the structural information, we introduce a novel method, the Persistence Intensity Array, for studying complex traits using tools from the emergent field of Topological Data Analysis. This approach uses the complete geometry of the phenotype and represents it as a simpler summary of the key topological shape features contained in the data. We demonstrate this method's efficacy by through a simulated QTL analysis.

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“…A thorough introduction to TDA can be found in [4] and in the review papers [3,5]. A conceptual introduction to TDA may be found in [6].…”

Section: Introductionmentioning

confidence: 99%