A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies.

In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

Single cell expression and chromatin accessibility of the Toxoplasma gondii lytic cycle identifies AP2XII-8 as an essential ribosome regulon driver.

Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite's unique division by internal budding, short cell cycle, and jumbled up classically defined cell cycle stages have restrained in-depth transcriptional program analysis. Here, unbiased transcriptome and chromatin accessibility maps throughout the lytic cell cycle are established at the single-cell level. Correlated pseudo-timeline assemblies of expression and chromatin profiles maps transcriptional versus chromatin level transition points promoting the cell division cycle. Sequential clustering analysis identifies functionally related gene groups promoting cell cycle progression. Promoter DNA motif mapping reveals patterns of combinatorial regulation. Pseudo-time trajectory analysis reveals transcriptional bursts at different cell cycle points. The dominant burst in G1 is driven largely by transcription factor AP2XII-8, which engages a conserved DNA motif, and promotes the expression of 44 ribosomal proteins encoding regulon. Overall, the study provides integrated, multi-level insights into apicomplexan transcriptional regulation.

Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes.

Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional transcriptomics datasets are weakly powered with sample sizes in the tens of patients. Therefore, highly regularized statistical approaches are essential to making generalizable predictions. At the same time, prior knowledge-driven approaches have been successfully applied to the manual interpretation of high-dimensional transcriptomics datasets. In this work, we assess the impact of combining two orthogonal approaches for the discovery of biomarker signatures, namely (1) well-known lasso-based regression approaches and its more recent derivative, the group lasso, and (2) the discovery of significant upstream regulators in literature-derived biological networks. Our method integrates both approaches in a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Using nested cross-validation as well as independent clinical datasets, we demonstrate that our approach leads to increased accuracy and generalizable results. We implement our approach in a computationally efficient, user-friendly R package called creNET. The package can be downloaded at https://github.com/kouroshz/creNethttps://github.com/kouroshz/creNet and is accompanied by a parsed version of the STRING DB data base.

A general benevolence dimension that links neural, psychological, economic, and life-span data on altruistic tendencies.

[Correction Notice: An Erratum for this article was reported in Vol 145(10) of Journal of Experimental Psychology: General (see record 2016-46925-004). In the article, there was an error in the Task, Stimuli, and Procedures section. In the 1st sentence in the 6th paragraph, "Following the scanning phase, participants completed self-report questionnaires meant to reflected the Prosocial Disposition construct: the agreeableness scale from the Big F, which includes empathic concern and perspective-taking, and a scale of personality descriptive adjectives related to altruistic behavior (Wood, Nye, & Saucier, 2010)." should have read: "Following the scanning phase, participants completed self-report questionnaires that contained scales to reflect the Prosocial Disposition construct: the Big Five Inventory (BFI; John et al., 1991), from which we used the agreeableness scale to measure prosocial disposition; the Interpersonal Reactivity Index (IRI; Davis, 1980), from which we used the empathic concern and perspective-taking scales; and a scale of personality descriptive adjectives related to altruistic behavior (Wood, Nye, & Saucier, 2010)."] Individual and life span differences in charitable giving are an important economic force, yet the underlying motives are not well understood. In an adult, life span sample, we assessed manifestations of prosocial tendencies across 3 different measurement domains: (a) psychological self-report measures, (b) actual giving choices, and (c) fMRI-derived, neural indicators of "pure altruism." The latter expressed individuals' activity in neural valuation areas when charities received money compared to when oneself received money and thus reflected an altruistic concern for others. Results based both on structural equation modeling and unit-weighted aggregate scores revealed a strong higher-order General Benevolence dimension that accounted for variability across all measurement domains. The fact that the neural measures likely reflect pure altruistic tendencies indicates that General Benevolence is based on a genuine concern for others. Furthermore, General Benevolence exhibited a robust increase across the adult life span, potentially providing an explanation for why older adults typically contribute more to the public good than young adults.