DeepIMB: Imputation of non-biological zero counts in microbiome data

The high prevalence of non-biological zero counts, arising from low sequencing depth and sampling variation, presents a significant challenge in microbiome data analysis. These zeros can distort taxon abundance distributions and hinder the identification of true biological signals, complicating downstream analyses.

To address the challenges of non-biological zeros in microbiome datasets, we propose DeepIMB, a deep learning-based imputation method for microbiome data, specifically designed to accurately identify and impute non-biological zero counts while preserving biological integrity.

DeepIMB operates in two main phases. First, it identifies non-biological zeros using a gamma-normal mixture model applied to the normalized, log-transformed taxon count matrix. Second, it imputes these zeros with a deep neural network model that integrates diverse sources of information, including taxon abundances, sample covariates, and phylogenetic distances, thereby learning complex, nonlinear relationships within microbiome data.

By leveraging integrated information from multiple data types, DeepIMB accurately imputes non-biological zeros while preserving true biological signals. In our two simulation studies, DeepIMB outperformed existing imputation methods in terms of mean squared error, Pearson correlation coefficient, and Wasserstein distance.

DeepIMB effectively addresses the challenges posed by non-biological zeros in microbiome data. By improving the quality of the data and the reliability of downstream analyses, DeepIMB represents a significant advancement in microbiome research methodologies.