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ABSTRACT We develop a data‐driven cosegmentation algorithm of passively sensed and self‐reported active variables collected through smartphones to identify emotionally stressful states in middle‐aged and older patients with mood disorders undergoing therapy, some of whom also have chronic pain. Our method leverages the association between the different types of time series. These data are typically nonstationary, with meaningful associations often occurring only over short time windows. Traditional machine learning (ML) methods, when applied globally on the entire time series, often fail to capture these time‐varying local patterns. Our approach first segments the passive sensing variables by detecting their change points, then examines segment‐specific associations with the active variable to identify cosegmented periods that exhibit distinct relationships between stress and passively sensed measures. We then use these periods to predict future emotional stress states using standard ML methods. By shifting the unit of analysis from individual time points to data‐driven segments of time and allowing for different associations in different segments, our algorithm helps detect patterns that only exist within short‐time windows. We apply our method to detect periods of stress in patient data collected during ALACRITY Phase I study. Our findings indicate that the data‐driven segmentation algorithm identifies stress periods more accurately than traditional ML methods that do not incorporate segmentation. 

Abstract MotivationThere is a growing interest in longitudinal omics data paired with some longitudinal clinical outcome. Given a large set of continuous omics variables and some continuous clinical outcome, each measured for a few subjects at only a few time points, we seek to identify those variables that co-vary over time with the outcome. To motivate this problem we study a dataset with hundreds of urinary metabolites along with Tuberculosis mycobacterial load as our clinical outcome, with the objective of identifying potential biomarkers for disease progression. For such data clinicians usually apply simple linear mixed effects models which often lack power given the low number of replicates and time points. We propose a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method [Li and Li (Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 2008;24:1175–82.)] to a longitudinal group lasso + Laplacian approach. Our method, PROLONG, leverages the first differences of the data to increase power by pairing the consecutive time points. The Laplacian penalty incorporates the dependence structure of the variables, and the group lasso penalty induces sparsity while grouping together all contemporaneous and lag terms for each omic variable in the model. ResultsWith an automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and sensitivity across a wide range of scenarios. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA. Availability and implementationAn R package implementing described methods called “prolong” is available at https://github.com/stevebroll/prolong. Code snapshot available at 10.5281/zenodo.14804245. 

Abstract BackgroundImmune responses need to be initiated rapidly, and maintained as needed, to prevent establishment and growth of infections. At the same time, resources need to be balanced with other physiological processes. On the level of transcription, studies have shown that this balancing act is reflected in tight control of the initiation kinetics and shutdown dynamics of specific immune genes. ResultsTo investigate genome-wide expression dynamics and trade-offs after infection at a high temporal resolution, we performed an RNA-seq time course onD. melanogasterwith 20 time points post Imd stimulation. A combination of methods, including spline fitting, cluster analysis, and Granger causality inference, allowed detailed dissection of expression profiles, lead-lag interactions, and functional annotation of genes through guilt-by-association. We identified Imd-responsive genes and co-expressed, less well characterized genes, with an immediate-early response and sustained up-regulation up to 5 days after stimulation. In contrast, stress response and Toll-responsive genes, among which were Bomanins, demonstrated early and transient responses. We further observed a strong trade-off with metabolic genes, which strikingly recovered to pre-infection levels before the immune response was fully resolved. ConclusionsThis high-dimensional dataset enabled the comprehensive study of immune response dynamics through the parallel application of multiple temporal data analysis methods. The well annotated data set should also serve as a useful resource for further investigation of theD. melanogasterinnate immune response, and for the development of methods for analysis of a post-stress transcriptional response time-series at whole-genome scale.