Phylogenetic Convolutional Neural Networks in Metagenomics

TL;DR

This paper introduces Ph-CNN, a novel deep learning architecture that applies convolutional neural networks to metagenomics data by using phylogenetic distances as a measure of neighborhood, enabling effective classification of microbiome samples.

Contribution

The paper presents Ph-CNN, a new deep learning model that incorporates phylogenetic distances into CNNs for metagenomics data classification, a novel approach in this domain.

Findings

Ph-CNN outperforms classical algorithms like SVM and Random Forest.

The method achieves promising classification accuracy on gut microbiota data.

Implementation as a custom Keras layer allows transparent neighborhood passing.

Abstract

Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Results: Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Conclusion: Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user. Keywords: Metagenomics; Deep learning; Convolutional Neural Networks; Phylogenetic trees