DepMicroDiff: Diffusion-Based Dependency-Aware Multimodal Imputation for Microbiome Data

TL;DR

DepMicroDiff introduces a diffusion-based, dependency-aware framework combining transformers and LLM-encoded metadata to improve microbiome data imputation, outperforming existing methods across multiple cancer datasets.

Contribution

It presents a novel diffusion-based model with dependency-aware transformers and LLM-encoded metadata, enhancing microbiome imputation accuracy and robustness.

Findings

Achieves higher Pearson correlation (up to 0.712)

Attains cosine similarity up to 0.812

Demonstrates robustness across diverse cancer datasets

Abstract

Microbiome data analysis is essential for understanding host health and disease, yet its inherent sparsity and noise pose major challenges for accurate imputation, hindering downstream tasks such as biomarker discovery. Existing imputation methods, including recent diffusion-based models, often fail to capture the complex interdependencies between microbial taxa and overlook contextual metadata that can inform imputation. We introduce DepMicroDiff, a novel framework that combines diffusion-based generative modeling with a Dependency-Aware Transformer (DAT) to explicitly capture both mutual pairwise dependencies and autoregressive relationships. DepMicroDiff is further enhanced by VAE-based pretraining across diverse cancer datasets and conditioning on patient metadata encoded via a large language model (LLM). Experiments on TCGA microbiome datasets show that DepMicroDiff substantially outperforms state-of-the-art baselines, achieving higher Pearson correlation (up to 0.712), cosine similarity (up to 0.812), and lower RMSE and MAE across multiple cancer types, demonstrating its robustness and generalizability for microbiome imputation.