Deep Learning Methods for Omics Data Imputation

Simple Summary Missing values are common in omics data and can arise from various causes. Imputation approaches offer a different means of handling missing data instead of utilizing only subsets of the dataset. However, the imputation of missing omics data is a challenging task. Advanced imputation methods such as deep learning-based approaches can model complex patterns and relationships in large and high-dimensional omics datasets, making them an increasingly popular choice for imputation. This review provides an overview of deep learning-based methods for omics data imputation, focusing on model architectures and multi-omics data imputation. This review also examines the challenges and opportunities that are associated with deep learning methods in this field.Abstract One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.