Visualizing structure and transitions in high-dimensional biological data

By Kevin R. Moon, D. van Dijk, Zheng Wang, S. Gigante, Daniel B. Burkhardt, William S. Chen, Kristina M. Yim, A. V. D. Elzen, Matthew J. Hirn, Ronald R. Coifman and 3 more. 2019

1,097 citations recorded by the source corpus.

The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data. PHATE, a new data visualization tool, better preserves patterns in high-dimensional data after dimensionality reduction.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Dmitry Kobak
2. Malte D. Luecken
3. Luke Zappia
4. H. Schiller
5. Fabian J Theis
6. Philipp Berens
7. John Healy
8. Leland McInnes

In Computer Vision and Pattern Recognition

1. Dmitry Kobak
2. Philipp Berens
3. J. Böhm
4. Fabian J Theis
5. Leland McInnes
6. Eric J. Topol
7. Pranav Rajpurkar
8. Alexander Tong

In Molecular Biology

1. Malte D. Luecken
2. Dmitry Kobak
3. J. Böhm
4. Fabian J Theis
5. Luke Zappia
6. Philipp Berens
7. H. Schiller
8. Anna C. Schaar

Citations in the corpus

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018, methodology): “PHATE [42] is a good example of a hybrid approach that begins with local structure information and makes use of MDS to aŠempt to preserve long scale distances as well.”
• Multimodal biomedical AI (2022, methodology): “Another approach is dimensionality reduction, including novel methods such as PHATE and Multiscale PHATE, which can learn abstract representations of biological and clinical data at different levels of granularity, and have been shown to predict clinical outcomes, for example, in people with coronav”
• Improving and generalizing flow-based generative models with minibatch optimal transport (2023, neutral): “We also include the Embryoid-body data from Moon et al. (2019); Tong et al. (2020).”
• A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum (2020, methodology): “Methods based on coarse-graining, such as e.g. PHATE (Moon et al., 2019) or latent variable NE method in Saul (2020) may behave di ff erently.”
• TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics (2020, methodology): “Figure 7 shows this data projected into two dimensions using a non-linear dimensionality reduction method called PHATE (Moon et al., 2019).”
• Best practices for single-cell analysis across modalities (2023, background): “As a next analysis milestone, different cellular populations can be identified to further guide and structure the analysis (Fig.”