Biology subjects

Spakowicz, D. J.

Publications and source records attributed to Spakowicz, D. J..

3 recordsLinked to original sources

bioRxiv2025

mt.surv: Multi-Threshold Survival Analysis for Associating Continuous Predictor Variables with Time-to-Event Outcomes

MotivationTime-to-event models are a useful and common approach to infer the importance of biological variables. Most often, the predictor variable is binarized and associated with censored time to death--a so-called Kaplan-Meier curve. However, the threshold to binarize the continuous variable is often arbitrary. We sought a rigorous way to define thresholds and evaluate the strength and consistency of association with the event of interest. ResultsWe present {mt.surv}, an R package for multi-threshold survival analyses. The primary function performs a time-to-event analysis, where a continuous predictor variable is stratified into two groups at an arbitrary number of places, defined by the percentile of the distribution. The result can be visualized with a function that creates a custom line plot with the -log of a log-likelihood p-value against the threshold percentile. A third function operates on this plot and calculates the area above a significance threshold, creating a scalar that can be used to rank biological variables for their association with the event of interest. This framework can be broadly applied to any continuous predictor variable including, but not limited to, gene expression and microbial abundances. Several helper functions are included for structuring input data and job submission in a cluster framework. We found that this method has value in discovery-type analyses where one lacks prior information about appropriate stratification thresholds. However, we found additional biological insight is possible-- indeed, quite common--as many variables show different associations at different stratification thresholds. AvailabilityOn CRAN as {mt.surv}. Contactdaniel.spakowicz@osumc.edu

bioinformatics↗

bioRxiv2024

Dynamic Human Gut Microbiome and Immune Shifts During an Immersive Psychosocial Therapeutic Program

BackgroundDepression is a leading cause of disability worldwide yet its underlying factors, particularly microbial associations, are poorly understood. MethodsWe examined the longitudinal interplay between the microbiome and immune system in the context of depression during an immersive psychosocial intervention. 142 multi-omics samples were collected from 52 well-characterized participants before, during, and three months after a nine-day inquiry-based stress reduction program. ResultsWe found that depression was associated with both an increased presence of putatively pathogenic bacteria and reduced microbial beta-diversity. Following the intervention, we observed reductions in neuroinflammatory cytokines and improvements in several mental health indicators. Interestingly, participants with a Prevotella-dominant microbiome showed milder symptoms when depressed, along with a more resilient microbiome and more favorable inflammatory cytokine profile, including reduced levels of CXCL-1. ConclusionsOur findings reveal a protective link between the Prevotella-dominant microbiome and depression, associated with a less inflammatory environment and moderated symptoms. These insights, coupled with observed improvements in neuroinflammatory markers and mental health from the intervention, highlight potential avenues for microbiome-targeted therapies in depression management.

microbiology↗

bioRxiv2020

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise interventions effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

bioinformatics↗