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

Hanbyul Song¹, Md Mozaffar Hosain², Taesung Park^3,4

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.

Genes & Genomics

|November 6, 2025

Summary

DeepIMB, a novel deep learning method, accurately imputes non-biological zero counts in microbiome data. This approach enhances data quality and improves the reliability of microbiome analyses.

Keywords:

Deep learning Differential abundance Imputation Microbiome Zero inflation

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Area of Science:

Microbiome research
Bioinformatics
Computational biology

Background:

Non-biological zero counts are prevalent in microbiome data due to low sequencing depth and sampling variation.
These zeros distort taxon abundance, obscure true biological signals, and complicate downstream analyses.

Purpose of the Study:

To introduce DeepIMB, a deep learning imputation method for microbiome data.
To accurately identify and impute non-biological zeros while preserving biological integrity.

Main Methods:

DeepIMB uses a two-phase approach: identifying non-biological zeros with a gamma-normal mixture model and imputing them using a deep neural network.
The imputation model integrates taxon abundances, sample covariates, and phylogenetic distances to capture complex data relationships.

Main Results:

DeepIMB accurately imputes non-biological zeros and preserves genuine biological signals.
Simulation studies demonstrated DeepIMB's superior performance over existing methods in terms of mean squared error, Pearson correlation, and Wasserstein distance.

Conclusions:

DeepIMB effectively resolves the issue of non-biological zeros in microbiome datasets.
This method advances microbiome research by improving data quality and the reliability of subsequent analyses.