Biology subjects

Sohn, M. B.

Publications and source records attributed to Sohn, M. B..

3 recordsLinked to original sources

bioRxiv2024

A semi-parametric multiple imputation method for high-sparse, high-dimensional, compositional data

High sparsity (i.e., excessive zeros) in microbiome data, which are high-dimensional and compositional, is unavoidable and can significantly alter analysis results. However, efforts to address this high sparsity have been very limited because, in part, it is impossible to justify the validity of any such methods, as zeros in microbiome data arise from multiple sources (e.g., true absence, stochastic nature of sampling). The most common approach is to treat all zeros as structural zeros (i.e., true absence) or rounded zeros (i.e., undetected due to detection limit). However, this approach can underestimate the mean abundance while overestimating its variance because many zeros can arise from the stochastic nature of sampling and/or functional redundancy (i.e., different microbes can perform the same functions), thus losing power. In this manuscript, we argue that treating all zeros as missing values would not significantly alter analysis results if the proportion of structural zeros is similar for all taxa, and we propose a semi-parametric multiple imputation method for high-sparse, high-dimensional, compositional data. We demonstrate the merits of the proposed method and its beneficial effects on downstream analyses in extensive simulation studies. We reanalyzed a type II diabetes (T2D) dataset to determine differentially abundant species between T2D patients and non-diabetic controls.

bioinformatics↗

bioRxiv2024

Affective Symptoms in Pregnancy are Associated with the Vaginal Microbiome

Composition of the vaginal microbiome in pregnancy is associated with adverse maternal, obstetric, and child health outcomes. Identifying the sources of individual differences in the vaginal microbiome is therefore of considerable clinical and public health interest. The current study tested the hypothesis that vaginal microbiome composition during pregnancy is associated with an individuals experience of affective symptoms and stress exposure. Data were based on a prospective longitudinal study of a diverse and medically healthy community sample of 275 mother-infant pairs. Affective symptoms and stress exposure and select measures of associated biomarkers (diurnal salivary cortisol, serum measures of sex hormones) were collected at each trimester; self-report, clinical, and medical records were used to collect detailed data on socio-demographic factors and health behavior, including diet and sleep. Vaginal microbiome samples were collected in the third trimester (34-40 weeks) and characterized by 16S rRNA sequencing. Identified taxa were clustered into three community state types (CST1-3) based on dissimilarity of vaginal microbiota composition. Results indicate that depressive symptoms during pregnancy were reliably associated with individual taxa and CST3 in the third trimester. Prediction of functional potential from 16S taxonomy revealed a differential abundance of metabolic pathways in CST1-3 and individual taxa, including biosynthetic pathways for the neuroactive metabolites, serotonin and dopamine. With the exception of bioavailable testosterone, no significant associations were found between symptoms- and stress-related biomarkers and CSTs. Our results provide further evidence of how prenatal psychological distress during pregnancy alters the maternal-fetal microbiome ecosystem that may be important for understanding maternal and child health outcomes. ImportancePrenatal affective symptoms and stress are associated with maternal, obstetric, and child health outcomes, but the mechanisms underlying these links and their application to intervention remain unclear. The findings from this investigation extend prior microbiome-oriented research by demonstrating that the maternal vaginal microbiome composition has a biologically plausible mechanistic link with affective symptoms that also suggest additional clinical applications for assessment and intervention.

microbiology↗

bioRxiv2017

Compositional Mediation Analysis for Microbiome Studies

Motivated by recent advances in causal mediation analysis and problems in the analysis of microbiome data, we consider the setting where the effect of a treatment on an outcome is transmitted through perturbing the microbial communities or compositional mediators. Compositional and high-dimensional nature of such mediators makes the standard mediation analysis not directly applicable to our setting. We propose a sparse compositional mediation model that can be used to estimate the causal direct and indirect (or mediation) effects utilizing the algebra for compositional data in the simplex space. We also propose tests of total and component-wise mediation effects using bootstrap. We conduct extensive simulation studies to assess the performance of the proposed method and apply the method to a real metagenomic dataset to investigate the effect of fat intake on body mass index mediated through the gut microbiome composition.

bioinformatics↗