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

Cristian Padron-Manrique^1,2, Aarón Vázquez-Jiménez¹, Diego Armando Esquivel-Hernandez¹

¹Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, 14610, Mexico.

Bioinformatics (Oxford, England)

|November 28, 2023

Summary

This summary is machine-generated.

Microbiota data noise and sparsity are addressed by mb-PHENIX, a new Python algorithm. It recovers missing microbial taxa and reveals differences between groups, improving microbiome data analysis.

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Microbiota data analysis faces challenges including technical noise, high dimensionality, and zero-inflated abundance matrices.
These issues compromise the reliability of scientific findings and hinder the identification of differential abundance microbes.

Purpose of the Study:

To develop an advanced algorithm for denoising microbiota data and recovering missing taxa abundances.
To improve the accuracy of differential abundance analysis in microbiome studies.

Main Methods:

mb-PHENIX is an open-source Python algorithm utilizing imputation via diffusion.
It employs supervised Uniform Manifold Approximation Projection (sUMAP) for space initialization.
This hybrid machine learning approach addresses noisy and sparse count matrices in 16S and shotgun sequencing data.

Main Results:

mb-PHENIX effectively recovers missing taxa abundances from sparse and noisy microbiota datasets.
The algorithm successfully denoises microbiome data, enabling the detection of differential abundance microbes.
It outperforms traditional abundance analysis methods in revealing microbial differences among study groups.

Conclusions:

mb-PHENIX offers a robust solution for handling common challenges in microbiota data analysis.
The algorithm enhances the reliability and interpretability of microbiome studies.
mb-PHENIX is accessible via GitHub with a Google Colab implementation.