Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples

Boggust, Angie; Carter, Brandon; Satyanarayan, Arvind

doi:10.1145/3490099.3511122

Cited by 38 publications

(32 citation statements)

References 49 publications

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“…Compare embedding spaces. Most existing embedding-space comparison techniques assume that the correspondences between the two embedding spaces are clear [6,54]. For example, they compare the embeddings of the same group of words generated by different embedding algorithms.…”

Section: Design Considerations and Implicationsmentioning

confidence: 99%

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Cheng

Keller

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The interface of Polyphony contains three views: the comparison view (A), the anchor set view (B), and the marker view (C).The comparison view provides an overview of the joint embedding space, and offers users interactions to inspect (A1), delete (A2), and add (A3) anchors. The anchor set view orders the anchors in a table, supporting inspection and comparison of different anchors (B1-2). The marker view shows the significant genes (C1) for the query and reference cells from a focal anchor.

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Section: Design Considerations and Implicationsmentioning

confidence: 99%

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Cheng

Keller

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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“…The tool mainly focuses on comparing embedding space properties, such as neighborhood overlap or spread and neighbor distances. The Embedding Comparator system [12] shares this objective, differentiating from embComp in the fact that it simultaneously visualizes global views of embedding structure alongside local views of individual objects and their common and unique neighbors to enable efficient analysis. Vector embedding comparison is also supported by Emblaze [52] which consists of an elaborate interactive scatterplot and mainly focuses on neighborhood discovery for dynamic relation suggestions.…”

Section: Related Workmentioning

confidence: 99%

BiaScope: Visual Unfairness Diagnosis for Graph Embeddings

Rissaki¹,

Scarone²,

Liu³

et al. 2022

Preprint

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Fig. 1. BIASCOPE is an interactive tool for visual unfairness diagnosis for graph embeddings. The tool consists of three views: (A) a Statistical Summary View, (B) an Unfairness Comparison View and (C) a Diagnose View. The content of the views is as follows: (A) The Statistical Summary View summarizes key properties of the selected network. (B) The Unfairness Comparison View is a side-by-side view that facilitates the fairness comparison of two embeddings of the same network. Nodes are colored based on their unfairness score, where darker nodes are embedded more unfairly. For example, the figure shows that the observed bias can be evenly distributed throughout the network or concentrated in certain communities. (C) The Diagnose View shows the embedding subspace affecting the unfairness score of a selected node, together with its ego network. The two visualizations are interactively linked, allowing the user to investigate how bias appears in an embedding and how it relates to the relevant graph structure.

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“…We compared the VeloViz embeddings to more conventional PC, t-SNE, UMAP, and diffusion map embeddings. To evaluate how accurately each embedding captured the ground truth trajectory, we calculated a trajectory consistency (TC) score (Supplementary Information 3, (Boggust et al, 2019)) where high TC scores indicate more accurate representations of the ground truth trajectory. For the simulated cycling trajectory, all evaluated embeddings were able to capture the cycling structure of the trajectory except for the PC embedding (Supplementary Figure 1A).…”

Section: Comparing Veloviz To Other Embeddingsmentioning

confidence: 99%

VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories

2021

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Motivation Single cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. Results We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Availability Source code is available on GitHub (http://github.com/JEFworks-Lab/veloviz) and Bioconductor (http://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Supplementary information Supplementary data are available at Bioinformatics online.

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Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples

Cited by 38 publications

References 49 publications

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

BiaScope: Visual Unfairness Diagnosis for Graph Embeddings

VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories

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