Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes

Wang, Zexuan; Yang, W.; Ryan, Katharine; Garai, Sumita; Auerbach, Benjamin M.; Shen, Li

doi:10.1109/bibm55620.2022.9995036

Cited by 3 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past several decades, numerous parametrization methods focusing on different properties have been developed: angle-preserving map, 16 area-preserving map, 17,18 or parametrization in order to preserve some forms of isometric distortion: balance between area and length distortions 19 or density-equalizing reference map. 20 In this paper, we propose to use novel spherical optimal transportation method 17,21 to perform spherical parametrization. We name this method as SPHARM-OT, standing for spherical harmonic representation using optimal transportation.…”

Section: Surface Modelingmentioning

confidence: 99%

Shape analysis of amygdala atrophy using SPHARM-OT

Wang

Chen

Yang

et al. 2023

Medical Imaging 2023: Image Processing

Self Cite

View full text Add to dashboard Cite

We propose SPHARM-OT, an enhanced spherical harmonic (SPHARM) surface modeling method using optimal transport (OT) for spherical parameterization. To demonstrate its effectiveness, we apply it to shape analysis of amygdala atrophy in Alzheimer's disease (AD). Of note, identifying morphological abnormalities of medial temporal structures such as hippocampus and amygdala is an important research topic for early diagnosis of AD. Our empirical study includes two steps: (1) the newly proposed SPHARM-OT method is used to model amygdala shapes of 101 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI); (2) using the random field theory to perform surface-based statistical analysis for detecting shape changes between cognitively normal (CN) and AD participants. We demonstrate that (1) the new SPHARM-OT method could effectively reduce surface mapping distortion and lead to a more accurate shape reconstruction result; and (2) significant shape changes between CN and AD participants are identified on certain amygdalar surface regions.

show abstract

Section: Surface Modelingmentioning

confidence: 99%

Shape analysis of amygdala atrophy using SPHARM-OT

Wang

Chen

Yang

et al. 2023

Medical Imaging 2023: Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In all cases, researchers should choose multiple measurements to represent any aspect of anatomy to be analyzed. In Wang et al, 2022), or automated phenotyping allows for the quantification of complex morphology (e.g., Hallgrímsson et al, 2020;Li et al, 2017), researchers will need to continue to be purposeful in the selection of measurements to be analyzed in evolutionary studies.…”

Section: Measurement Selectionmentioning

confidence: 99%

“…Nonetheless, by being explicit about the choice of dimensions to represent morphology, researchers are effectively establishing hypotheses about not only evolutionary questions concerning fitness, but about how we best represent morphological variation. Even when methods are developed that identify areas of high and low morphological variance within a functional context (Abdel Fatah et al, 2012; Wang et al, 2022), or automated phenotyping allows for the quantification of complex morphology (e.g., Hallgrímsson et al, 2020; Li et al, 2017), researchers will need to continue to be purposeful in the selection of measurements to be analyzed in evolutionary studies.…”

Section: Guidance On How To Use Measures Of Evolvability: What To Do ...mentioning

confidence: 99%

Morphology, evolution, and the whole organism imperative: Why evolutionary questions need multi‐trait evolutionary quantitative genetics

Auerbach

Savell

Agosto

2023

American Journal of Biological Anthropology

View full text Add to dashboard Cite

Since Washburn's New Physical Anthropology, researchers have sought to understand the complexities of morphological evolution among anatomical regions in human and non‐human primates. Researchers continue, however, to preferentially use comparative and functional approaches to examine complex traits, but these methods cannot address questions about evolutionary process and often conflate function with fitness. Moreover, researchers also tend to examine anatomical elements in isolation, which implicitly assumes independent evolution among different body regions. In this paper, we argue that questions asked in primate evolution are best examined using multiple anatomical regions subjected to model‐bound methods built from an understanding of evolutionary quantitative genetics. A nascent but expanding number of studies over the last two decades use this approach, examining morphological integration, evolvability, and selection modeling. To help readers learn how to use these methods, we review fundamentals of evolutionary processes within a quantitative genetic framework, explore the importance of neutral evolutionary theory, and explain the basics of evolutionary quantitative genetics, namely the calculation of evolutionary potential for multiple traits in response to selection. Leveraging these methods, we demonstrate their use to understand non‐independence in possible evolutionary responses across the limbs, limb girdles, and basicranium of humans. Our results show that model‐bound quantitative genetic methods can reveal unexpected genetic covariances among traits that create a novel but measurable understanding of evolutionary complexity among multiple traits. We advocate for evolutionary quantitative genetic methods to be a standard whenever appropriate to keep studies of primate morphological evolution relevant for the next seventy years and beyond.

show abstract

“…This concept has garnered considerable interest from theoretical and practical perspectives [7, 8]. In recent times, its applicability has been rediscovered and extended to fields such as image processing [9], shape analysis [10], generative modeling [11], and fairness [12]. At its core, OT addresses the challenges of comparing empirical distribution, ensuring mass preservation while minimizing associated costs.…”

Section: Introductionmentioning

confidence: 99%

“…This concept has garnered considerable interest from theoretical and practical perspectives [7,8]. In recent times, its applicability has been rediscovered and extended to fields such as image processing [9], shape analysis [10], generative *Corresponding author: li.shen@pennmedicine.upenn.edu Figure 1: Schematic design of the proposed QOT algorithm. (A) Given the sample matrix, Space X , each matrix within it represents the single-cell gene expression for a sample.…”

Section: Introductionmentioning

confidence: 99%

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

Wang,

Zhan,

Yang

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Single-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The data's innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

show abstract

Using Optimal Transport to Improve Spherical Harmonic Quantification of Complex Biological Shapes

Cited by 3 publications

References 28 publications

Shape analysis of amygdala atrophy using SPHARM-OT

Shape analysis of amygdala atrophy using SPHARM-OT

Morphology, evolution, and the whole organism imperative: Why evolutionary questions need multi‐trait evolutionary quantitative genetics

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

Contact Info

Product

Resources

About