Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data

Zhou, Youjia; Chalapathi, Nithin; Rathore, Archit; Zhao, Yi; Wang, Bei

doi:10.1109/pacificvis52677.2021.00021

Cited by 17 publications

(12 citation statements)

References 12 publications

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“…Recent works have fused graph neural network techniques with Mapper to consume graph-structured data as input and return meaningful embeddings ( Bodnar et al, 2020 ), and such a module could be easily inserted between the reciprocal k NN and intrinsic binning steps in our framework to take advantage of the representational power of neural networks. Along the lines of hardware-driven scalability, Mapper Interactive ( Zhou et al, n.d. ) provides state-of-the-art GPU implementations of the Mapper algorithm for embedding dimensions 1 and 2, and our method could be adapted to fit into such a pipeline. In addition to such scalability improvements, semiautomated mesoscale network structure analysis is a fundamental aspect of our pipeline, and we have demonstrated multiple ways in which a user can supply annotations (i.e., based on task structure or NeuroSynth meta-analyses) to yield quantitative results from data.…”

Section: Discussionmentioning

confidence: 99%

NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

Geniesse

Chowdhury

Saggar

2022

Network Neuroscience

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For better translational outcomes researchers and clinicians alike demand novel tools to distil complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction, and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper—designed specifically for neuroimaging data—that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic “ongoing” cognition. Looking forward, we hope our framework could help researchers push the boundaries of psychiatric neuroimaging towards generating insights at the single-participant level while scaling across consortium-size datasets.

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Section: Discussionmentioning

confidence: 99%

NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

Geniesse

Chowdhury

Saggar

2022

Network Neuroscience

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“…The main differences are that (a) Pheno-Mapper offers more interactive capabilities in exploring phenomics data via its mapper graph representation, including easily extensible GUI, and (b) it provides easily extensible data analysis and ML capabilities including regression, feature selection, and dimensionality reduction. As a domain-specific adaptation of Mapper Interactive [22], Pheno-Mapper inherits a number of properties including extendability and interactivity. With these additional capabilities, Pheno-Mapper is well suited for the analysis and visualization of phenomics data, in particular, for exploring the subpopulations.…”

Section: Related Workmentioning

confidence: 99%

“…Such information can be used for further analysis, such as comparisons between different subpopulations. [22] -is implemented using the standard HTML, CSS, Javascript stack with D3.js, and JQuery libraries. It is equipped with a Python backend using a Flask-based server.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…Pheno-Mapper is a domain-specific adaptation of Mapper Interactive [22], which is an interactive, extendable, and scalable toolbox for exploring generic, high-dimensional point clouds. We extend the original toolbox of Mapper Interactive by adding new capabilities that are desirable for studying phenomics datasets.…”

Section: Design and Implementationmentioning

confidence: 99%

“…In this paper, we present a software tool called Pheno-Mapper that is designed to overcome the above challenges associated with phenomics data analysis. Pheno-Mapper is a domain-specific adaptation of a toolbox called the Mapper Interactive [22] and specifically targets the analysis and visualization of multidimensional phenomics data. More specifically, in comparison to existing approaches, the main advantage of Pheno-Mapper is two-fold.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pheno-Mapper: An Interactive Toolbox for the Visual Exploration of Phenomics Data

Zhou,

Kamruzzaman,

Schnable

et al. 2021

Preprint

Self Cite

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High-throughput technologies to collect field data have made observations possible at scale in several branches of life sciences. The data collected can range from the molecular level (genotypes) to physiological (phenotypic traits) and environmental observations (e.g., weather, soil conditions). These vast swathes of data, collectively referred to as phenomics data, represent a treasure trove of key scientific knowledge on the dynamics of the underlying biological system. However, extracting information and insights from these complex datasets remains a significant challenge owing to their multidimensionality and lack of prior knowledge about their complex structure. In this paper, we present Pheno-Mapper, an interactive toolbox for the exploratory analysis and visualization of large-scale phenomics data. Our approach uses the mapper framework to perform a topological analysis of the data, and subsequently render visual representations with built-in data analysis and machine learning capabilities. We demonstrate the utility of this new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to existing approaches, the main advantage of Pheno-Mapper is that it provides rich, interactive capabilities in the exploratory analysis of phenomics data, and it integrates visual analytics with data analysis and machine learning in an easily extensible way. In particular, Pheno-Mapper allows the interactive selection of subpopulations guided by a topological summary of the data and applies data mining and machine learning to these selected subpopulations for in-depth exploration.

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Stitch Fix for Mapper and Topological Gains

Zhou

Saul

Safarli

et al. 2022

Association for Women in Mathematics Series

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Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data

Cited by 17 publications

References 12 publications

NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

Pheno-Mapper: An Interactive Toolbox for the Visual Exploration of Phenomics Data

Stitch Fix for Mapper and Topological Gains

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