A novel deep learning method for predictive modeling of microbiome data

Abstract

With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as drug response predictions and disease diagnosis. It is thus essential and desirable to build a prediction model for clinical outcomes based on microbiome data that usually consist of taxon abundance and a phylogenetic tree. Importantly, all microbial species are not uniformly distributed in the phylogenetic tree but tend to be clustered at different phylogenetic depths. Therefore, the phylogenetic tree represents a unique correlation structure of microbiome, which can be an important prior to improve the prediction performance. However, prediction methods that consider the phylogenetic tree in an efficient and rigorous way are under-developed. Here, we develop a novel deep learning prediction method MDeep (microbiome-based deep learning method) to predict both continuous and binary outcomes. Conceptually, MDeep designs convolutional layers to mimic taxonomic ranks with multiple convolutional filters on each convolutional layer to capture the phylogenetic correlation among microbial species in a local receptive field and maintain the correlation structure across different convolutional layers via feature mapping. Taken together, the convolutional layers with its built-in convolutional filters capture microbial signals at different taxonomic levels while encouraging local smoothing and preserving local connectivity induced by the phylogenetic tree. We use both simulation studies and real data applications to demonstrate that MDeep outperforms competing methods in both regression and binary classifications. Availability and Implementation: MDeep software is available at https://github.com/lichen-lab/MDeep Contact:chen61@iu.edu

Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease

“…Other ML model architectures, such as convolutional neural networks (CNNs), are commonly used for classification problems with certain structure in the input data, such as image classification. In the context of microbiome data, the CNN MDeep adds structure to OTU features through hierarchical agglomerative clustering of the phylogeny-induced correlation between OTUs [58]. As MDeep is currently only developed for OTU features, we assessed whether this CNN architecture led to greater classification performance with OTU abundance than our MLP architecture.…”

“…We implemented MDeep, a CNN architecture recently designed for microbiome data [58]. CNNs require an inherent structure to present in the data, which is added to the OTU dataset by hierarchical agglomerative clustering of the phylogeny-induced correlation between OTUs.…”

Application of Deep Learning in Plant–Microbiota Association Analysis

“…The classification of microbial species, the prediction of host phenotypes and ecological environments, the investigation of interactions between community members, and the prediction of associations between microbiome and disease are the key tasks (Qu et al, 2019;Zhou and Gallins, 2019). More and more DL models show advantages in human metagenomics data in detecting biomarkers that characterize the microbiome traits and host phenotype, such as MetaPheno (LaPierre et al, 2019) and MDeep (Wang et al, 2020). The ML method has succeeded in predicting productivity based on plant soil metagenomic data.…”

“…The MDeep model has been developed to simulate the phylogenetic tree structure of microbial taxa at different taxonomical levels. It indicated that convolutional neural network could automatically learn representation and map a complex feature to the simple one by convolutional multilayers (Wang et al, 2020).…”

Incorporating genome-based phylogeny and functional similarity into diversity assessments helps to resolve a global collection of human gut metagenomes

“…Even with these caveats, many studies have utilized phylogenetic measures of diversity to differentiate microbiomes by host disease state or other phenotype (9,10) . Moreover, phylogenies are successfully utilized in many recently published tools for microbiome data analysis, which include pseudo-count imputation (11) , dataset augmentation (12) , constrained ordination analysis (13) , transforming compositional abundance data (14) , and machine learning models for predicting host phenotypes (15,16) .…”