Abstract
Microbe-disease association relationship mining is drawing more and more attention due to its potential in capturing disease-related microbes. Hence, it is essential to develop new tools or algorithms to study the complex pathogenic mechanism of microbe-related diseases. However, previous research studies mainly focused on the paradigm of “one disease, one microbe,” rarely investigated the cooperation and associations between microbes, diseases or microbe-disease co-modules from system level. In this study, we propose a novel two-level module identifying algorithm (MDNMF) based on nonnegative matrix tri-factorization which integrates two similarity matrices (disease and microbe similarity matrices) and one microbe-disease association matrix into the objective of MDNMF. MDNMF can identify the modules from different levels and reveal the connections between these modules. In order to improve the efficiency and effectiveness of MDNMF, we also introduce human symptoms-disease network and microbial phylogenetic distance into this model. Furthermore, we applied it to HMDAD dataset and compared it with two NMF-based methods to demonstrate its effectiveness. The experimental results show that MDNMF can obtain better performance in terms of enrichment index (EI) and the number of significantly enriched taxon sets. This demonstrates the potential of MDNMF in capturing microbial modules that have significantly biological function implications.
Highlights
With the development of high-throughput sequencing technology, such as 16S ribosomal RNA (16S rRNA), more and more microbes were identified
The diagonal elements of s can be used to evaluate the quality of clustering, and the off-diagonal elements can be used to establish the possible connections between different modules
We adopted enrichment index (EI) and the number of significantly enriched microbe taxon set (TSsig) as metrics to evaluate the performance of different algorithms
Summary
With the development of high-throughput sequencing technology, such as 16S ribosomal RNA (16S rRNA), more and more microbes were identified. Jorth et al have reported that gene expression profiles of periodontitisrelated microbial communities have highly conserved changes, relative to healthy samples (Jorth et al, 2014). It means that microbiome composition changes in oral cavity could be associated with pathogenesis of periodontitis. Chen et al have observed that the colonization with Helicobacter pylori has negative correlation with the symptom of allergy (pollens and molds), especially in the childhood (Chen and Blaser, 2007; Blaser, 2014) All these reveal the potential association between pathogenic microorganisms and complex human diseases
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