Eric F. Lock

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Research in several fields now requires the analysis of datasets in which multiple high-dimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular two-block methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals gene-miRNA associations and provides better characterization of tumor types.

The genomic landscape of mantle cell lymphoma is related to the epigenetically determined chromatin state of normal B cells

In this study, we define the genetic landscape of mantle cell lymphoma (MCL) through exome sequencing of 56 cases of MCL. We identified recurrent mutations in ATM, CCND1, MLL2, and TP53. We further identified a number of novel genes recurrently mutated in patients with MCL including RB1, WHSC1, POT1, and SMARCA4. We noted that MCLs have a distinct mutational profile compared with lymphomas from other B-cell stages. The ENCODE project has defined the chromatin structure of many cell types. However, a similar characterization of primary human mature B cells has been lacking. We defined, for the first time, the chromatin structure of primary human naïve, germinal center, and memory B cells through chromatin immunoprecipitation and sequencing for H3K4me1, H3K4me3, H3Ac, H3K36me3, H3K27me3, and PolII. We found that somatic mutations that occur more frequently in either MCLs or Burkitt lymphomas were associated with open chromatin in their respective B cells of origin, naïve B cells, and germinal center B cells. Our work thus elucidates the landscape of gene-coding mutations in MCL and the critical interplay between epigenetic alterations associated with B-cell differentiation and the acquisition of somatic mutations in cancer.

Interstrain differences in the liver effects of trichloroethylene in a multistrain panel of inbred mice

Trichloroethylene (TCE) is a widely used industrial chemical and a common environmental contaminant. It is a well-known carcinogen in rodents and a probable carcinogen in humans. Studies utilizing panels of mouse inbred strains afford a unique opportunity to understand both metabolic and genetic basis for differences in responses to TCE. We tested the hypothesis that strain- and liver-specific toxic effects of TCE are genetically controlled and that the mechanisms of toxicity and susceptibility can be uncovered by exploring responses to TCE using a diverse panel of inbred mouse strains. TCE (2100 mg/kg) or corn oil vehicle was administered by gavage to 6- to 8-week-old male mice of 15 mouse strains. Serum and liver were collected at 2, 8, and 24 h postdosing and were analyzed for TCE metabolites, hepatocellular injury, and gene expression of liver. TCE metabolism, as evident from the levels of individual oxidative and conjugative metabolites, varied considerably between strains. TCE treatment-specific effect on the liver transcriptome was strongly dependent on genetic background. Peroxisome proliferator-activated receptor-mediated molecular networks, consisting of the metabolism genes known to be induced by TCE, represent some of the most pronounced molecular effects of TCE treatment in mouse liver that are dependent on genetic background. Conversely, cell death, liver necrosis, and immune-mediated response pathways, which are altered by TCE treatment in liver, are largely genetic background independent. These studies provide better understanding of the mechanisms of TCE-induced toxicity anchored on metabolism and genotype-phenotype correlations that may define susceptibility or resistance.

ToxPi GUI

MOTIVATION Scientists and regulators are often faced with complex decisions, where use of scarce resources must be prioritized using collections of diverse information. The Toxicological Prioritization Index (ToxPi™) was developed to enable integration of multiple sources of evidence on exposure and/or safety, transformed into transparent visual rankings to facilitate decision making. The rankings and associated graphical profiles can be used to prioritize resources in various decision contexts, such as testing chemical toxicity or assessing similarity of predicted compound bioactivity profiles. The amount and types of information available to decision makers are increasing exponentially, while the complex decisions must rely on specialized domain knowledge across multiple criteria of varying importance. Thus, the ToxPi bridges a gap, combining rigorous aggregation of evidence with ease of communication to stakeholders. RESULTS An interactive ToxPi graphical user interface (GUI) application has been implemented to allow straightforward decision support across a variety of decision-making contexts in environmental health. The GUI allows users to easily import and recombine data, then analyze, visualize, highlight, export and communicate ToxPi results. It also provides a statistical metric of stability for both individual ToxPi scores and relative prioritized ranks. AVAILABILITY The ToxPi GUI application, complete user manual and example data files are freely available from http://comptox.unc.edu/toxpi.php.

Efficiency clustering for low-density microarrays and its application to QPCR

BackgroundPathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency.ResultsOur results indicate that QPCR primer-pairs differ significantly both in reliability and efficiency. They can only be used in an array format if the raw data (so called CT values for real-time QPCR) are transformed to take these differences into account. We developed a novel method to obtain efficiency-adjusted CT values. We introduce transformed confidence intervals as a novel measure to identify unreliable primers. We introduce a robust clustering algorithm to combine efficiencies of groups of probes, and our results indicate that using n < 10 cluster-based mean efficiencies is comparable to using individually determined efficiency adjustments for each primer pair (N = 96-1024).ConclusionsCareful estimation of primer efficiency is necessary to avoid significant measurement inaccuracies. Transformed confidence intervals are a novel method to assess and interprete the reliability of an efficiency estimate in a high throughput format. Efficiency clustering as developed here serves as a compromise between the imprecision in assuming uniform efficiency, and the computational complexity and danger of over-fitting when using individually determined efficiencies.

R.JIVE for exploration of multi-source molecular data

UNLABELLED : The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION R.JIVE is available via the Comprehensive R Archive Network (CRAN) under the GPLv3 license: https://cran.r-project.org/web/packages/r.jive/ CONTACT elock@umn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Metabolomic analysis of CSF indicates brain metabolic impairment precedes hematological indices of anemia in the iron-deficient infant monkey

Objectives: Iron deficiency (ID) anemia leads to long-term neurodevelopmental deficits by altering iron-dependent brain metabolism. The objective of the study was to determine if ID induces metabolomic abnormalities in the cerebrospinal fluid (CSF) in the pre-anemic stage and to ascertain the aspects of abnormal brain metabolism affected. Methods: Standard hematological parameters [hemoglobin (Hgb), mean corpuscular volume (MCV), transferrin (Tf) saturation, and zinc protoporphyrin/heme (ZnPP/H)] were compared at 2, 4, 6, 8, and 12 months in iron-sufficient (IS; n = 7) and iron-deficient (ID; n = 7) infant rhesus monkeys. Five CSF metabolite ratios were determined at 4, 8, and 12 months using 1H NMR spectroscopy at 16.4 T and compared between groups and in relation to hematologic parameters. Results: ID infants developed ID (Tf saturation < 25%) by 4 months of age and all became anemic (Hgb < 110 g/L and MCV < 60 fL) at 6 months. Their heme indices normalized by 12 months. Pyruvate/glutamine and phosphocreatine/creatine (PCr/Cr) ratios in CSF were lower in the ID infants by 4 months (P < 0.05). The PCr/Cr ratio remained lower at 8 months (P = 0.02). ZnPP/H, an established blood marker of pre-anemic ID, was positively correlated with the CSF citrate/glutamine ratio (marginal correlation, 0.34; P < 0.001; family wise error rate = 0.001). Discussion: Metabolomic analysis of the CSF is sensitive for detecting the effects of pre-anemic ID on brain energy metabolism. Persistence of a lower PCr/Cr ratio at 8 months, even as hematological measures demonstrated recovery from anemia, indicate that the restoration of brain energy metabolism is delayed. Metabolomic platforms offer a useful tool for early detection of the impact of ID on brain metabolism in infants.

Identification of Chiari Type I Malformation subtypes using whole genome expression profiles and cranial base morphometrics.

BackgroundChiari Type I Malformation (CMI) is characterized by herniation of the cerebellar tonsils through the foramen magnum at the base of the skull, resulting in significant neurologic morbidity. As CMI patients display a high degree of clinical variability and multiple mechanisms have been proposed for tonsillar herniation, it is hypothesized that this heterogeneous disorder is due to multiple genetic and environmental factors. The purpose of the present study was to gain a better understanding of what factors contribute to this heterogeneity by using an unsupervised statistical approach to define disease subtypes within a case-only pediatric population.MethodsA collection of forty-four pediatric CMI patients were ascertained to identify disease subtypes using whole genome expression profiles generated from patient blood and dura mater tissue samples, and radiological data consisting of posterior fossa (PF) morphometrics. Sparse k-means clustering and an extension to accommodate multiple data sources were used to cluster patients into more homogeneous groups using biological and radiological data both individually and collectively.ResultsAll clustering analyses resulted in the significant identification of patient classes, with the pure biological classes derived from patient blood and dura mater samples demonstrating the strongest evidence. Those patient classes were further characterized by identifying enriched biological pathways, as well as correlated cranial base morphological and clinical traits.ConclusionsOur results implicate several strong biological candidates warranting further investigation from the dura expression analysis and also identified a blood gene expression profile corresponding to a global down-regulation in protein synthesis.

Exploratory analysis of exercise adherence patterns with sedentary pregnant women.

Background: It is not well understood how sedentary women who wish to engage in regular exercise adhere to interventions during pregnancy and what factors may influence adherence over time. Objective: The aim of this study was to examine longitudinal patterns of pregnant womens adherence to exercise. Methods: Exploratory secondary data analyses were carried out with 124 previously sedentary pregnant women (ages 31 ± 5 years; 85% non-Hispanic White) from a randomized controlled trial. Daily exercise logs (n = 92) from 18 through 35 weeks of gestation were explored using linear regression, functional data, and principal component analyses. Results: Adherence decreased as gestation week increased (p < .001). The top adherers maintained levels of adherence, and the bottom adherers decreased levels of adherence. And adherence pattern was influenced by types of exercise throughout the study period. Discussion: Exercise behavior patterns were explored in a randomized controlled trial study, using chronometric data on exercise attendance. A new analytic approach revealed that sedentary pregnant women may adopt exercise habits differently from other populations.

Shared kernel Bayesian screening

This article concerns testing for equality of distribution between groups. We focus on screening variables with shared distributional features such as common support, modes and patterns of skewness. We propose a Bayesian testing method using kernel mixtures, which improves performance by borrowing information across the different variables and groups through shared kernels and a common probability of group differences. The inclusion of shared kernels in a finite mixture, with Dirichlet priors on the weights, leads to a simple framework for testing that scales well for high-dimensional data. We provide closed asymptotic forms for the posterior probability of equivalence in two groups and prove consistency under model misspecification. The method is applied to DNA methylation array data from a breast cancer study, and compares favourably to competitors when Type I error is estimated via permutation.