Identification of Functional Microbial Modules Through Network-Based Analysis of Meta-Microbial Features Using Matrix Factorization

Rank selection for non-negative matrix factorization

Non-Negative Matrix Factorization (NMF) is a widely used dimension reduction method that factorizes a non-negative data matrix into two lower dimensional non-negative matrices: one is the basis or feature matrix which consists of the variables and the other is the coefficients matrix which is the projections of data points to the new basis. The features can be interpreted as sub-structures of the data. The number of sub-structures in the feature matrix is also called the rank. This parameter controls the model complexity and is the only tuning parameter for the NMF model. An appropriate rank will extract the key latent features while minimizing the noise from the original data. However due to the large amount of optimization error always present in the NMF computation, the rank selection has been a difficult problem. We develop a novel rank selection method based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately. Through simulations, we compare our method with a rank selection method based on hypothesis testing using bootstrap distribution without deconvolution and a method based on cross-validation; we demonstrate that our method is not only accurate at estimating the true ranks for NMF, especially when the features are hard to distinguish, but also efficient at computation. When applied to real microbiome data (eg, OTU data and functional metagenomic data), our method also shows the ability to extract interpretable subcommunities in the data.

Emerging frontiers of antibiotics use and their impacts on the human gut microbiome

Antibiotics, the primary drugs used to cure bacterial diseases, are increasingly becoming ineffective due to the emergence of multiple drug resistance (MDR) leading to recurrence of previously sensitive pathogens. Human gut microbiome (GM), known to play an important role in various physiological processes, consists of pool of diverse microbes. Indiscriminate use of antibiotics during the life span of an individual may lead to development of resistant microbes e.g. Vibrio, Acinetobacter, Escherichia, Klebsiella, Clostridia, etc. in the human GM. Transmission of antibiotic resistant genes (ARGs) between pathogenic and commensal bacteria occurs more frequently in microbiome communities wherein bacteria communicate and exchange cellular constituents both among themselves and with the host. Additionally, co-factors like 'early vs. late' exposure, type of antibiotics and duration of treatment modulate the adverse effects of antibiotics on GM maturation. Furthermore, factors like mode of birth, ethnicity, malnutrition, demography, diet, lifestyle, etc., which influence GM composition, can also indirectly alter the host response to antibiotics. Currently, advanced 'omics' and culturomics approaches are revealing novel avenues to study the interplay between antibiotics and the microbiome and to identify resistant genes in these bacterial communities. Here, we discuss the recent developments that have given insights into the effects of antibiotics on the homeostatic balance of the gut microbiome and thus on human health.