Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Kuchroo, Manik; Huang, Jessie; Wong, Patrick; Grenier, Jean; Shung, Dennis; Tong, Alexander; Lucas, Carolina; Klein, Jon; Burkhardt, Daniel; Gigante, Scott; Godavarthi, Abhinav; Israelow, Benjamin; Mao, Tianyang; Oh, Ji Eun; Silva, Julio; Takahashi, Takehiro; Odio, Camila D.; Casanovas‐Massana, Arnau; Fournier, John; Farhadian, Shelli; Cruz, Charles S. Dela; Ko, Albert Icksang; Wilson, F. Perry; Hussin, Julie; Wolf, Guy; Iwasaki, Akiko; Krishnaswamy, Smita

doi:10.1101/2020.11.15.383661

Cited by 10 publications

(14 citation statements)

References 98 publications

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“…This is not a desirable attribute for early detection of new differentiated sub-lineages, which are often sampled in lower numbers compared to the other well-established lineages. We thus explored the use of msPHATE on SARS-CoV-2 genetic data, a novel unsupervised learning technique that showed promising results on biological data (13), to enable the identification of additional structure within haplotype groups using whole viral consensus sequences (Step 8, Figure 1). The method creates a tree of data granularities that can be cut at coarse levels for high-level summarizations of data, or at fine levels for detailed representations on subsets.…”

Section: Resultsmentioning

confidence: 99%

“…The code is available at https://github.com/HussinLab/covid19_mostefai2021_paper. Finally, msPHATE (13) (package available at https://github.com/KrishnaswamyLab/Multiscale_PHATE) embeddings were computed for the first and second waves GISAID high quality consensus sequences. A 0123 encoded matrix was used for each wave to generate the MS-PHATE embeddings.…”

Section: Methodsmentioning

confidence: 99%

“…Uniform Manifold Approximation and Projection (UMAP), an unsupervised learning technique that has become increasingly popular in genomics in recent years, was previously used on GISAID sequences of SARS-CoV-2 (12). More recently, a novel unsupervised method, Multiscale PHATE (msPHATE) (13), was used for COVID-19 clinical data in combination with SARS-CoV-2 genetic data (14).…”

Section: Introductionmentioning

confidence: 99%

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Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Mostefai

Gamache

Huang

et al. 2021

Preprint

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The genome of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), the pathogen that causes coronavirus disease 2019 (COVID-19), has been sequenced at an unprecedented scale, leading to a tremendous amount of viral genome sequencing data. To understand the evolution of this virus in humans, and to assist in tracing infection pathways and designing preventive strategies, we present a set of computational tools that span phylogenomics, population genetics and machine learning approaches. To illustrate the utility of this toolbox, we detail an in depth analysis of the genetic diversity of SARS-CoV-2 in first year of the COVID-19 pandemic, using 329,854 high-quality consensus sequences published in the GISAID database during the pre-vaccination phase. We demonstrate that, compared to standard phylogenetic approaches, haplotype networks can be computed efficiently on much larger datasets, enabling real-time analyses. Furthermore, time series change of Tajima's D provides a powerful metric of population expansion. Unsupervised learning techniques further highlight key steps in variant detection and facilitate the study of the role of this genomic variation in the context of SARS-CoV-2 infection, with Multiscale PHATE methodology identifying fine-scale structure in the SARS-CoV-2 genetic data that underlies the emergence of key lineages. The computational framework presented here is useful for real-time genomic surveillance of SARS-CoV-2 and could be applied to any pathogen that threatens the health of worldwide populations of humans and other organisms.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Mostefai

Gamache

Huang

et al. 2021

Preprint

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“…2) suggests that the walktrap algorithm provides a flexible and robust way to define super-cells. Practically, we recommend using ∈ [10,50], as this already provides a significant speed-up in the analysis of large datasets.…”

Section: Discussionmentioning

confidence: 99%

Metacells untangle large and complex single-cell transcriptome networks

Bilous

Tran

Cianciaruso

et al. 2021

Preprint

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Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. Here we develop a network-based coarse-graining framework where highly similar cells are merged into super-cells. We demonstrate that super-cells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, super-cells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop.

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“…Then, users proceed to expand clusters and investigate lower (and more detailed) hierarchy levels. Although HDR has been an active area of research in the past few years [10,17,32], current techniques present a few limitations. HiPP [30] uses nodes-encoded as circles with varying areas-to create an overview of the dataset and guide users in the further analysis.…”

Section: Introductionmentioning

confidence: 99%

HUMAP: Hierarchical Uniform Manifold Approximation and Projection

Marcílio-Jr¹,

Eler²,

Paulovich³

et al. 2021

Preprint

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Fig. 1: Comparison of traditional dimensionality reduction (DR) techniques (A) vs. hierarchical DR techniques (B).Traditional techniques operate in a single level of detail and focus on delivering a big picture that depicts the dataset as a whole, ignoring fine-grained details of intra-cluster distributions and making it difficult to proceed with more detailed investigations. On the other hand, hierarchical DR (HDR) approaches summarize important intra-cluster information already on the top-level representations of the hierarchy imposed on the input data without affecting the layout's overall organization. Our hierarchical approach, called HUMAP, extends HDR techniques state of the art by preserving the mental map when requesting more details of clusters while balancing the trade-off between global and local relations. This characteristic is apparent in the first three layouts for hierarchical DR results (B). For HUMAP, the projected data subsets in different hierarchy levels resemble superior hierarchy levels, while users receive more information about the similarity among clusters. Furthermore, HUMAP also preserves the mental map of different hierarchy levels; that is, the top-level representation (B) resembles the lowest-level representation (C).

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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Cited by 10 publications

References 98 publications

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Data-driven approaches for genetic characterization of SARS-CoV-2 lineages

Metacells untangle large and complex single-cell transcriptome networks

HUMAP: Hierarchical Uniform Manifold Approximation and Projection

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