mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data

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Abstract

Motivation

Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure.

Results

We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails.

Availability and implementation

The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

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Most cited references 8

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UMAP: Uniform Manifold Approximation and Projection

Leland McInnes, John Healy, Nathaniel Saul … (2018)

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Christine L Chaffer, Kevin R. Moon, Cassandra Burdziak … (2018)

Single-cell RNA-sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed ‘dropout’, which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov Affinity-based Graph Imputation of Cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures and uncovers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations. One Sentence Summary: Graph diffusion-based imputation method recovers missing transcripts in scRNA-seq data, yielding insight into the epithelial-to-mesenchymal transition. Abstract highlights: 1. MAGIC restores noisy and sparse single-cell data using diffusion geometry. 2. Corrected data is amenable to myriad downstream analyses. 3. MAGIC enables archetypal analysis and inference of gene interactions. 4. Transcription factor targets can be predicted without perturbation after MAGIC. In brief - A new algorithm overcomes limitations of data loss in single cell sequencing experiments

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Uniform Manifold Approximation and Projection (UMAP) Reveals Composite Patterns and Resolves Visualization Artifacts in Microbiome Data

George Armstrong, Cameron Martino, Gibraan Rahman … (2021)

Microbiome data are sparse and high dimensional, so effective visualization of these data requires dimensionality reduction. To date, the most commonly used method for dimensionality reduction in the microbiome is calculation of between-sample microbial differences (beta diversity), followed by principal-coordinate analysis (PCoA). Uniform Manifold Approximation and Projection (UMAP) is an alternative method that can reduce the dimensionality of beta diversity distance matrices. Here, we demonstrate the benefits and limitations of using UMAP for dimensionality reduction on microbiome data. Using real data, we demonstrate that UMAP can improve the representation of clusters, especially when the clusters are composed of multiple subgroups. Additionally, we show that UMAP provides improved correlation of biological variation along a gradient with a reduced number of coordinates of the resulting embedding. Finally, we provide parameter recommendations that emphasize the preservation of global geometry. We therefore conclude that UMAP should be routinely used as a complementary visualization method for microbiome beta diversity studies. IMPORTANCE UMAP provides an additional method to visualize microbiome data. The method is extensible to any beta diversity metric used with PCoA, and our results demonstrate that UMAP can indeed improve visualization quality and correspondence with biological and technical variables of interest. The software to perform this analysis is available under an open-source license and can be obtained at https://github.com/knightlab-analyses/umap-microbiome-benchmarking ; additionally, we have provided a QIIME 2 plugin for UMAP at https://github.com/biocore/q2-umap .

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Author and article information

Contributors

Cristian Padron-Manrique: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Aarón Vázquez-Jiménez: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Diego Armando Esquivel-Hernandez: Role: ConceptualizationRole: Data curationRole: InvestigationRole: Writing - original draftRole: Writing - review & editing

Yoscelina Estrella Martinez Lopez

Daniel Neri-Rosario: Role: InvestigationRole: Writing - original draftRole: Writing - review & editing

Jean Paul Sánchez-Castañeda: Role: InvestigationRole: Writing - original draftRole: Writing - review & editing

David Giron-Villalobos: Role: InvestigationRole: Writing - original draftRole: Writing - review & editing

Osbaldo Resendis-Antonio:

ORCID: https://orcid.org/0000-0001-5220-541X

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing

Pier Luigi Martelli: Role: Associate Editor

Journal

Journal ID (nlm-ta): Bioinformatics

Journal ID (iso-abbrev): Bioinformatics

Journal ID (publisher-id): bioinformatics

Title: Bioinformatics

Publisher: Oxford University Press

ISSN (Print): 1367-4803

ISSN (Electronic): 1367-4811

Publication date Collection: December 2023

Publication date (Electronic): 28 November 2023

Publication date PMC-release: 28 November 2023

Volume: 39

Issue: 12

Electronic Location Identifier: btad706