Modular analysis of gene expression data with R.

SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

Respiratory motion artifact suppression in diffusion-weighted MR imaging of the spine.

Diffusion-weighted spin-echo imaging of the spine has been successfully implemented for differentiation of benign fracture edema and tumor infiltration of the vertebral body. Nevertheless, this technique still suffers from insufficient image quality in numerous patients due to motion artifacts. The aim of this study was to investigate the impact of variable respiratory motion artifact suppression techniques on image quality in diffusion-weighted spin-echo imaging of the spine. In addition to phase-encoding reordering, a newly implemented right hemi-diaphragmaitc navigator for respiratory gating was used. Subjective and objective image quality parameters were compared. Respiratory motion artifact suppression has a major impact on image quality in diffusion-weighted imaging of the spine. Phase-encoding reordering does not enhance image quality while right hemi-diaphragmatic respiratory navigator gating significantly improves image quality at the cost of data acquisition time. Navigator gating should be used if standard spin-echo diffusion-weighted imaging demonstrates insufficient image quality.

Current genetic data do not improve the prediction of type 2 diabetes mellitus: the CoLaus study.

CONTEXT: Several genetic risk scores to identify asymptomatic subjects at high risk of developing type 2 diabetes mellitus (T2DM) have been proposed, but it is unclear whether they add extra information to risk scores based on clinical and biological data. OBJECTIVE: The objective of the study was to assess the extra clinical value of genetic risk scores in predicting the occurrence of T2DM. DESIGN: This was a prospective study, with a mean follow-up time of 5 yr. SETTING AND SUBJECTS: The study included 2824 nondiabetic participants (1548 women, 52 ± 10 yr). MAIN OUTCOME MEASURE: Six genetic risk scores for T2DM were tested. Four were derived from the literature and two were created combining all (n = 24) or shared (n = 9) single-nucleotide polymorphisms of the previous scores. A previously validated clinic + biological risk score for T2DM was used as reference. RESULTS: Two hundred seven participants (7.3%) developed T2DM during follow-up. On bivariate analysis, no differences were found for all but one genetic score between nondiabetic and diabetic participants. After adjusting for the validated clinic + biological risk score, none of the genetic scores improved discrimination, as assessed by changes in the area under the receiver-operating characteristic curve (range -0.4 to -0.1%), sensitivity (-2.9 to -1.0%), specificity (0.0-0.1%), and positive (-6.6 to +0.7%) and negative (-0.2 to 0.0%) predictive values. Similarly, no improvement in T2DM risk prediction was found: net reclassification index ranging from -5.3 to -1.6% and nonsignificant (P ≥ 0.49) integrated discrimination improvement. CONCLUSIONS: In this study, adding genetic information to a previously validated clinic + biological score does not seem to improve the prediction of T2DM.

Data Envelopment Analysis (DEA) : un guide pédagogique à l'intention des décideurs dans le secteur public

Ce guide présente la méthode Data Envelopment Analysis (DEA), une méthode d'évaluation de la performance . Il est destiné aux responsables d'organisations publiques qui ne sont pas familiers avec les notions d'optimisation mathématique, autrement dit de recherche opérationnelle. L'utilisation des mathématiques est par conséquent réduite au minimum. Ce guide est fortement orienté vers la pratique. Il permet aux décideurs de réaliser leurs propres analyses d'efficience et d'interpréter facilement les résultats obtenus. La méthode DEA est un outil d'analyse et d'aide à la décision dans les domaines suivants : - en calculant un score d'efficience, elle indique si une organisation dispose d'une marge d'amélioration ; - en fixant des valeurs-cibles, elle indique de combien les inputs doivent être réduits et les outputs augmentés pour qu'une organisation devienne efficiente ; - en identifiant le type de rendements d'échelle, elle indique si une organisation doit augmenter ou au contraire réduire sa taille pour minimiser son coût moyen de production ; - en identifiant les pairs de référence, elle désigne quelles organisations disposent des best practice à analyser.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

A prospective, randomized, open label, phase iii clinical trial of novottf-100a vs best standard of care chemotherapy in patients with recurrent glioblastoma

BACKGROUND: Glioblastoma, the most common adult primary malignant brain tumor, confers poor prognosis (median survival of 15 months) notwithstanding aggressive treatment. Combination chemotherapy including carmustine (BCNU) or temozolomide (TMZ) with the MGMT inhibitor O6-benzylguanine (O6BG) has been used, but has been associated with dose-limiting hematopoietic toxicity. OBJECTIVE: To assess safety and efficacy of a retroviral vector encoding the O6BG-resistant MGMTP140K gene for transduction and autologous transplantation of hematopoietic stem cells (HSCs) in MGMT unmethylated, newly diagnosed glioblastoma patients in an attempt to chemoprotect bone marrowduring combination O6BG/TMZ therapy. METHODS: Three patients have been enrolled in the first cohort. Patients underwent standard radiation therapy without TMZ followed by G-CSF mobilization, apheresis, and conditioning with 600 mg/m2 BCNU prior to infusion of gene-modified cells. Posttransplant, patients were treated with 28-day cycles of single doseTMZ (472 mg/m2) with 48-hour intravenous O6BG (120 mg/m2 bolus, then 30 mg/m2/d). RESULTS: The BCNU dose was nonmyeloablative with ANC ,500/mL for ≤3 d and nadir thrombocytopenia of 28,000/mL. Gene marking in pre-infusion colony forming units (CFUs) was 70.6%, 79.0%, and 74.0% in Patients 1, 2, and 3, respectively, by CFU-PCR. Following engraftment, gene marking in white blood cells and sorted granulocytes ranged between 0.37-0.84 and 0.33-0.83 provirus copies, respectively, by real-time PCR. Posttransplant gene marking in CFUs from CD34-selected cells ranged from 28.5% to 47.4%. Patients have received 4, 3, and 2 cycles of O6BG/TMZ, respectively, with evidence for selection of gene-modified cells. One patient has received a single dose-escalated cycle at 590 mg/m2 TMZ. No additional extra-hematopoietic toxicity has been observed thus far and all three patients exhibit stable disease at 7-8 months since diagnosis CONCLUSIONS: We believe that these data demonstrate the feasibility of achieving significant engraftment of MGMTP140K-modified cells with a well-tolerated dose of BCNU. Further follow-up will determine whether this approach will allow for further dose escalation of TMZ and improved survival.

The C76R transmembrane activator and calcium modulator cyclophilin ligand interactor mutation disrupts antibody production and B-cell homeostasis in heterozygous and homozygous mice.

BackgroundMutations in TNFRSF13B, the gene encoding transmembrane activator and calcium modulator cyclophilin ligand interactor (TACI), are found in 10% of patients with common variable immunodeficiency. However, the most commonly detected mutation is the heterozygous change C104R, which is also found in 0.5% to 1% of healthy subjects. The contribution of the C104R mutation to the B-cell defects observed in patients with common variable immunodeficiency therefore remains unclear.ObjectiveWe sought to define the functional consequences of the C104R mutation on B-cell function.MethodsWe performed in vitro studies of TACI C104R expression and signaling. A knock-in mouse with the equivalent mutation murine TACI (mTACI) C76R was generated as a physiologically relevant model of human disease. We examined homozygous and heterozygous C76R mutant mice alongside wild-type littermates and studied specific B-cell lineages and antibody responses to T cell-independent and T cell-dependent challenge.ResultsC104R expression and ligand binding are significantly diminished when the mutant protein is expressed in 293T cells or in patients' cell lines. This leads to defective nuclear factor κB activation, which is proportionally restored by reintroduction of wild-type TACI. Mice heterozygous and homozygous for mTACI C76R exhibit significant B-cell dysfunction with splenomegaly, marginal zone B-cell expansion, diminished immunoglobulin production and serological responses to T cell-independent antigen, and abnormal immunoglobulin synthesis.ConclusionsThese data show that the C104R mutation and its murine equivalent, C76R, can significantly disrupt TACI function, probably through haploinsufficiency. Furthermore, the heterozygous C76R mutation alone is sufficient to disturb B-cell function with lymphoproliferation and immunoglobulin production defects.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

The UniProtKB/Swiss-Prot Tox-Prot program: A central hub of integrated venom protein data.

Animal toxins are of interest to a wide range of scientists, due to their numerous applications in pharmacology, neurology, hematology, medicine, and drug research. This, and to a lesser extent the development of new performing tools in transcriptomics and proteomics, has led to an increase in toxin discovery. In this context, providing publicly available data on animal toxins has become essential. The UniProtKB/Swiss-Prot Tox-Prot program (http://www.uniprot.org/program/Toxins) plays a crucial role by providing such an access to venom protein sequences and functions from all venomous species. This program has up to now curated more than 5000 venom proteins to the high-quality standards of UniProtKB/Swiss-Prot (release 2012_02). Proteins targeted by these toxins are also available in the knowledgebase. This paper describes in details the type of information provided by UniProtKB/Swiss-Prot for toxins, as well as the structured format of the knowledgebase.

Interpretation of array comparative genome hybridization data: a major challenge.

The advent and application of high-resolution array-based comparative genome hybridization (array CGH) has led to the detection of large numbers of copy number variants (CNVs) in patients with developmental delay and/or multiple congenital anomalies as well as in healthy individuals. The notion that CNVs are also abundantly present in the normal population challenges the interpretation of the clinical significance of detected CNVs in patients. In this review we will illustrate a general clinical workflow based on our own experience that can be used in routine diagnostics for the interpretation of CNVs.

Post-genomic update on a classical candidate gene for coronary artery disease: ESR1.

BACKGROUND: After age, sex is the most important risk factor for coronary artery disease (CAD). The mechanism through which women are protected from CAD is still largely unknown, but the observed sex difference suggests the involvement of the reproductive steroid hormone signaling system. Genetic association studies of the gene-encoding Estrogen Receptor α (ESR1) have shown conflicting results, although only a limited range of variation in the gene has been investigated. METHODS AND RESULTS: We exploited information made available by advanced new methods and resources in complex disease genetics to revisit the question of ESR1's role in risk of CAD. We performed a meta-analysis of 14 genome-wide association studies (CARDIoGRAM discovery analysis, N=≈87,000) to search for population-wide and sex-specific associations between CAD risk and common genetic variants throughout the coding, noncoding, and flanking regions of ESR1. In addition to samples from the MIGen (N=≈6000), WTCCC (N=≈7400), and Framingham (N=≈3700) studies, we extended this search to a larger number of common and uncommon variants by imputation into a panel of haplotypes constructed using data from the 1000 Genomes Project. Despite the widespread expression of ERα in vascular tissues, we found no evidence for involvement of common or low-frequency genetic variation throughout the ESR1 gene in modifying risk of CAD, either in the general population or as a function of sex. CONCLUSIONS: We suggest that future research on the genetic basis of sex-related differences in CAD risk should initially prioritize other genes in the reproductive steroid hormone biosynthesis system.