Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis

TL;DR

This paper introduces mbVDiT, a pre-trained conditional diffusion model that leverages patient metadata and variational autoencoders to improve microbiome data imputation and denoising, especially in cancer research.

Contribution

The paper presents a novel pre-trained conditional diffusion model that incorporates clinical metadata and VAE for enhanced microbiome data imputation and denoising.

Findings

Outperforms existing microbiome data imputation methods.

Effectively utilizes patient metadata for improved accuracy.

Demonstrates robustness across multiple cancer datasets.

Abstract

Emerging evidence indicates that human cancers are intricately linked to human microbiomes, forming an inseparable connection. However, due to limited sample sizes and significant data loss during collection for various reasons, some machine learning methods have been proposed to address the issue of missing data. These methods have not fully utilized the known clinical information of patients to enhance the accuracy of data imputation. Therefore, we introduce mbVDiT, a novel pre-trained conditional diffusion model for microbiome data imputation and denoising, which uses the unmasked data and patient metadata as conditional guidance for imputating missing values. It is also uses VAE to integrate the the other public microbiome datasets to enhance model performance. The results on the microbiome datasets from three different cancer types demonstrate the performance of our methods in comparison with existing methods.