Assessing Significance in High-Throughput Experiments by Sequential Goodness of Fit and q-Value Estimation

We developed a new multiple hypothesis testing adjustment called SGoF+ implemented as a sequential goodness of fit metatest which is a modification of a previous algorithm, SGoF, taking advantage of the information of the distribution of p-values in order to fix the rejection region. The new method uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. This new approach shows a better power/pFDR ratio than SGoF. In fact SGoF+ automatically sets the threshold leading to the maximum power and the minimum false non-discovery rate inside the SGoF' family of algorithms. Additionally, we suggest combining the information provided by SGoF+ with the estimate of the FDR that has been committed when rejecting a given set of nulls. We study different positive false discovery rate, pFDR, estimation methods to combine q-value estimates jointly with the information provided by the SGoF+ method. Simulations suggest that the combination of SGoF+ metatest with the q-value information is an interesting strategy to deal with multiple testing issues. These techniques are provided in the latest version of the SGoF+ software freely available at http://webs.uvigo.es/acraaj/SGoF.htm

NUEVAS APORTACIONES AL CATÁLOGO FAUNÍSTICO DE LOS QUIRONOMIDOS (DIPTERA: CHIRONOMIDAE) DEL PARQUE NACIONAL DE DOÑANA (SW DE ESPAÑA)

Twenty one species of chironomids have been recorded from Doñana National Park. Amongst these, 12 records are new for the area and 4 for the Iberian península. Total number of chironomid species known in the area rises to 35. A complete list of species including comments on their ecology and biogeography is includedEn los muestreos realizados para la ejecución del presente trabajo se han capturado ejemplares de 21 especies, de ellas 12 son nuevas citas para el Parque Nacional de Doñana y 4 para la Península Ibérica. Con la mencionada aportación, el número de especies de Quironómidos citados para el Parque asciende a 35. Se presenta una relación de las 21 especies capturadas con consideraciones de tipo ecológico y comentarios sobre las implicaciones biogeográficas de las especies más interesantes

GEOECOLOGICAL EVALUATION OF LOCAL SURROUNDINGS FOR THE PURPOSES OF RECREATIONAL TOURISM

The paper presents geoecological evaluation of the city of Loznica for the purposes of sports and recreational tourism based on quantitative method of diversity, V-Wert Method. Using the GIS tool by the quantitative method, the criteria of natural components (relief, forest, water surface and climate) are evaluated for the analyzed area. In the proposed method, the climate factor was supplemented by the analysis of the bioclimatic index Universal Thermal Climate Index (UTCI). When the evaluation was completed, the final results were obtained based on which the degrees of convenience of different parts of the analyzed area have been presented. Out of the total analyzed surface, which amounts to 705 km2, favorable surfaces comprise 21 km2 (2.98%), and very favorable surfaces comprise 33 km2, i.e. 4.68% of the territory. The largest area consists of conditionally favorable terrains — 333 km2 (47.23%). Since one of the basic strategic priorities of the City of Loznica is improvement and development of sports and recreational tourism, the aim of this analysis is to emphasize the potential of the mentioned area in terms of general suitability of the terrain for the development of this type of tourism

Nonparametric estimation of conditional transition probabilities in a non-Markov illness-death model

One important goal in multi-state modeling is the estimation of transition probabilities. In longitudinal medical studies these quantities are particularly of interest since they allow for long-term predictions of the process. In recent years signi ficant contributions have been made regarding this topic. However, most of the approaches assume independent censoring and do not account for the influence of covariates. The goal of the paper is to introduce feasible estimation methods for the transition probabilities in an illness-death model conditionally on current or past covariate measures. All approaches are evaluated through a simulation study, leading to a comparison of two di erent estimators. The proposed methods are illustrated using real a colon cancer data set.This research was nanced by FEDER Funds through Programa Operacional Factores de Competitividade COMPETE and by Portuguese Funds through FCT - Funda ção para a Cência e a Tecnologia, within Projects Est-C/MAT/UI0013/2011 and PTDC/MAT/104879/2008. We also acknowledge nancial support from the project Grants MTM2008-03129 and MTM2011-23204 (FEDER support included) of the Spanish Ministerio de Ciencia e Innovaci on and 10PXIB300068PR of the Xunta de Galicia. Partial support from a grant from the US National Security Agency (H98230-11-1-0168) is greatly appreciated

Relationship between damage and mortality in juvenile-onset systemic lupus erythematosus: Cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry (RELESSER)

Objectives: To identify patterns (clusters) of damage manifestation within a large cohort of juvenile SLE (jSLE) patients and evaluate their possible association with mortality. Methods: This is a multicentre, descriptive, cross-sectional study of a cohort of 345 jSLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index. Using cluster analysis, groups of patients with similar patterns of damage manifestation were identified and compared. Results: Mean age (years) ± S.D. at diagnosis was 14.2 ± 2.89; 88.7% were female and 93.4% were Caucasian. Mean SLICC/ACR DI ± S.D. was 1.27 ± 1.63. A total of 12 (3.5%) patients died. Three damage clusters were identified: Cluster 1 (72.7% of patients) presented a lower number of individuals with damage (22.3% vs. 100% in Clusters 2 and 3, P < 0.001); Cluster 2 (14.5% of patients) was characterized by renal damage in 60% of patients, significantly more than Clusters 1 and 3 (P < 0.001), in addition to increased more ocular, cardiovascular and gonadal damage; Cluster 3 (12.7%) was the only group with musculoskeletal damage (100%), significantly higher than in Clusters 1 and 2 (P < 0.001). The overall mortality rate in Cluster 2 was 2.2 times higher than that in Cluster 3 and 5 times higher than that in Cluster 1 (P < 0.017 for both comparisons). Conclusions: In a large cohort of jSLE patients, renal and musculoskeletal damage manifestations were the two dominant forms of damage by which patients were sorted into clinically meaningful clusters. We found two clusters of jSLE with important clinical damage that were associated with higher rates of mortality, especially for the cluster of patients with predominant renal damage. Physicians should be particularly vigilant to the early prevention of damage in this subset of jSLE patients with kidney involvement

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

<p>Abstract</p> <p>Background</p> <p>The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.</p> <p>Results</p> <p>It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.</p> <p>Conclusion</p> <p>Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.</p