Variance component score test for time-course gene set analysis of longitudinal RNA-seq data

As gene expression measurement technology is shifting from microarrays to sequencing, the statistical tools available for their analysis must be adapted since RNA-seq data are measured as counts. Recently, it has been proposed to tackle the count nature of these data by modeling log-count reads per million as continuous variables, using nonparametric regression to account for their inherent heteroscedasticity. Adopting such a framework, we propose tcgsaseq, a principled, model-free and efficient top-down method for detecting longitudinal changes in RNA-seq gene sets. Considering gene sets defined a priori, tcgsaseq identifies those whose expression vary over time, based on an original variance component score test accounting for both covariates and heteroscedasticity without assuming any specific parametric distribution for the transformed counts. We demonstrate that despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, and both may be computed quickly. A permutation version of the test is additionally proposed for very small sample sizes. Applied to both simulated data and two real datasets, the proposed method is shown to exhibit very good statistical properties, with an increase in stability and power when compared to state of the art methods ROAST, edgeR and DESeq2, which can fail to control the type I error under certain realistic settings. We have made the method available for the community in the R package tcgsaseq.Comment: 23 pages, 6 figures, typo corrections & acceptance acknowledgemen

Robust Evaluation of Longitudinal Surrogate Markers with Censored Data

The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g., fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the methods have not been developed for use with primary outcomes that are time-to-event outcomes and/or subject to censoring. In this paper, we propose robust methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting. Specifically, we propose a method to define and estimate the proportion of the treatment effect on a censored primary outcome that is explained by the treatment effect on a longitudinal surrogate marker measured up to time $t_0$. We accommodate both potential censoring of the primary outcome and of the surrogate marker. A simulation study demonstrates good finite-sample performance of our proposed methods. We illustrate our procedures by examining repeated measures of fasting plasma glucose, a surrogate marker for diabetes diagnosis, using data from the Diabetes Prevention Program (DPP)

Separation, for Analytical Purpose, of Np Traces from different Solutions of Fuel Reprocessing

AbstractFour separation methods were developed for performance control of hydrometallurgical extraction processes as COEX™ or advanced PUREX. These methods used implemented the operations of radionuclides oxidation state adjustment and chromatographic separation using TEVA resin. Concerning FP raffinate, the method consisted in reducing Np traces to the valence IV by a mixture of ferrous sulfamate and ascorbic acid, to fix Np(IV) on “TEVA” resin and to eluate it by a nitrohydrofluoric acid solution. The Np recovery yield is 100%. The decontamination of Np is sufficiently high to allow its analysis by FXL (Zr/Np < 1). The study also showed that in presence of Zr and Tc, Pu behaved like Np. The mixture of ferrous sulfamate and ascorbic acid had surprisingly no action on Pu(IV). Concerning plutonium solution ([Pu] > 10g/L) and uranium solution ([U] > 100g/L), the same method used for Np recovery from FP raffinate led to an eluate containing 100% of the initial Np ([Np]: 10mg/L). The low concentration of U and Pu (< 100mg/L) allows the determination of Np by FXL. Concerning Pu(III)-U(IV) solution, the method, included 2 redox stages, the first one to oxidize all actinides to oxidation state VI et the second one to reduce Np and Pu respectively to IV and III oxidation state. Then Np(IV) was fixed on TEVA resin. The eluate contains 100% of the initial Np ([Np]: 10mg/L) and a low concentration of U and Pu ([U] < 20mg/L, [Pu] < 10mg/L). The next experiments will consist in consolidating these good results by working with real solutions of fuel reprocessing

La gestion de l'environnement dans le Pacifique. Moyens et objectifs

La protection de l’environnement dans le Pacifique est une préoccupation relativement récente. Elle s’organise à partir des actions du Programme régional océanien de l’environnement (PROE), organisation régionale qui regroupe les États et territoires du Pacifique ainsi que les puissances occidentales qui ont des intérêts dans la région. Ses moyens, assez faibles, sont mobilisés par deux programmes principaux : le programme « écosystèmes insulaires » dont l’un des objectifs est la lutte contre les espèces bio-envahissantes et le programme « Avenirs océaniens » qui prend en compte la gestion de la pollution et des déchets et les changements climatiques.Managing and protecting Pacific’s Islands environment is quite a recent concern. It is organized around actions headed by the South Pacific Regional Environment Program (SPREP), a regional organisation which gathers, on one hand, Pacific’s Islands States and Territories, and, on the other hand, Western States which have interests in the region. The SPREP ressources, which are, comparatively, not so important, are dedicated to two main Programs : the «Islands ecosystems Program», which includes struggling against bio-invasives species, and the «Oceanian Futures» Program, which aims to control the dumping of toxic wastes, pollution and climatic changes

Statistical Methods for Multivariate and Complex Phenotypes

Many important scientific questions can not be studied properly using a single measurement as a response. For example, many phenotypes of interest in recent clinical research may be difficult to characterize due to their inherent complexity. It may be difficult to determine the presence or absence of disease based on a single measurement, or even a few measurements, or the phenotype may only be defined based on a series of symptoms. Similarly, a set of related phenotypes or measurements may be studied together in order to detect a shared etiology. In this work, we propose methods for studying complex phenotypes of these types, where the phenotype may be characterized either longitudinally or by a diverse set of continuous, discrete, or not fully observed components. In chapter 1, we seek to identify predictors that are related to multiple components of diverse outcomes. We take up specifically the question of identifying a multiple regulator, where we seek a genetic marker that is associated with multiple biomarkers for autoimmune disease. To do this, we propose sparse multiple regulation testing (SMRT) both to estimate the relationship between a set of predictors and diverse outcomes and to provide a testing framework in which to identify which predictors are associated with multiple elements of the outcomes, while controlling error rates. In chapter 2, we seek to identify risk profiles or risk scores for diverse outcomes, where a risk profile is a linear combination of predictors. The risk profiles will be chosen to be highly correlated to latent traits underlying the outcomes. To do this, we propose semiparametric canonical correlation analysis (sCCA), an updated version of the classical canonical correlation analysis. In chapter 3, the scientific question of interest pertains directly to the progression of disease over time. We provide a testing framework in which to detect the association between a set of genetic markers and the progression of disease in the context of a GWAS. To test for this association while allowing for highly nonlinear longitudinal progression of disease, we propose functional principal variance component (FPVC) testing

Gram-negative bacteraemia in non-ICU patients: factors associated with inadequate antibiotic therapy and impact on outcomes

Background: A considerable number of Gram-negative bacteraemias occur outside intensive care units (ICUs). Inadequate antibiotic therapy in ICUs has been associated with adverse outcomes; however, there are no prospective studies in non-ICU patients. Methods: A 6 month (1 August 2006–31 January 2007), prospective cohort study of non-ICU patients with Gram-negative bacteraemia in a tertiary-care hospital was performed. Inadequate empirical antibiotic therapy was defined as no antibiotic or starting a non-susceptible antibiotic within 24 h after the initial positive blood culture. Results: Two hundred and fifty non-ICU patients had Gram-negative bacteraemia. The mean age was 56.4 (+16.1) years. The predominant bacteria in monomicrobial infections were Escherichia coli (24%), Klebsiella pneumoniae (18%) and Pseudomonas aeruginosa (8%). Sixty-one (24%) patients had polymi-crobial bacteraemia. Seventy patients (28%) required ICU transfer and 35 (14%) died. Seventy-nine (31.6%) received inadequate empirical antibiotic therapy. These patients were more likely to have a hospital-acquired infection [odds ratio (OR)5 1.99, 95 % confidence interval (CI)5 1.11–3.56, P5 0.02] and less likely to have E. coli monomicrobial bacteraemia [OR 0.40 (95 % CI 0.19–0.86), P5 0.02]. There were no differences in occurrence of sepsis [72 (91.1%) patients with inadequate versus 159 (93.0%