An equivalence test between features lists, based on the Sorensen-Dice index and the joint frequencies of GO term enrichment.

BACKGROUND: In integrative bioinformatic analyses, it is of great interest to stablish the equivalence between gene or (more in general) feature lists, up to a given level and in terms of their annotations in the Gene Ontology. The aim of this article is to present an equivalence test based on the proportion of GO terms which are declared as enriched in both lists simultaneously. RESULTS: On the basis of these data, the dissimilarity between gene lists is measured by means of the Sorensen-Dice index. We present two flavours of the same test: One of them based on the asymptotic normality of the test statistic and the other based on the bootstrap method. CONCLUSIONS: The accuracy of these tests is studied by means of simulation and their possible interest is illustrated by using them over two real datasets: A collection of gene lists related to cancer and a collection of gene lists related to kidney rejection after transplantation.

Phenotypic diversity and history of the Congo Basin populations: Equatorial Guinea, Bantu Speaking Central Africans and African Pygmies.

BACKGROUND: African Pygmy Populations (APP) are believed to be one of the first groups of the Congo Basin rainforest peoples to separate from the rest of modern humanity. The arrival of Bantu speaking agriculturalists from 5,000 BP led to the formation of a large number of ethnic complexes in Central Africa and the Atlantic coastal area, mainly due to a one-way flow of pygmy women. There are now only a few small contingents of African Pygmy population groups who are mixed or who have almost disappeared as a result of migratory flows from Cameroon. AIMS: We analysed the adult phenotypic diversity of 9 populations (17 groups of both sexes) of the Congo basin and Bioko Island, which were anthropometrically characterised in 1948. The phenotypic clusters that we detected, which we interpret as likely mixing of local Pygmy populations and Bantus, may be useful as references for future studies, particularly genetics. SUBJECTS AND METHODS: The matrix of inter-distances between populations was generated, using the 21 variables, with the Euclidean distance between the mean vectors relating to the standardised variables. When the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) was used separately in men and women, the phylogenetic trees showed a clear separation between populations. RESULTS: The body measurements that most effectively distinguished the groups are linear dimensions and the width of the hips and mean thoracic circumference. In this study, the cephalo-facial dimensions were of little value in identifying the groups. CONCLUSIONS: The samples of Equatorial Guinea show a major inter-group overlap, and considerable intra-group variations. There are also notable differences amongst African Pygmy populations in terms of height, but not in body proportions.

An equivalence approach to the integrative analysis of feature lists.

BACKGROUND: Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of "enough similarity") between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit. RESULTS: In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine -as claimed by the authors- and (ii) a collection of pathogenesis-based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package. CONCLUSIONS: The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists.

Quantitative discrimination of deformation in Fueguian crania.

OBJECTIVES: Artificial deformation of the cranium in humans has been related to powerful environmental-cultural stimuli that modify vectors of growth and development when the cranium is still malleable. Osteological differentiation into deformed and nondeformed remains enables the morphological information of individuals that make up ethnic groups or populations to be separately contrasted, the invariant measurements of deformation to be identified, the information provided by archeological remains to be placed in a social context, the cranial variation to be related to genetic variation (individuals without deformation), and predictions to be made (in the absence of direct genetic information). METHODS: With samples of reduced size and many variables, we propose a decision rule based on: (a) pre-selecting variables (Kruskal-Wallis and Λ-Wilks test); (b) applying logistic regression to obtain the optimal classification criterion; and (c) defining a multi-criterion decision rule to bring about greater robustness. RESULTS: After applying the decision rule to a sample of 180 crania (71 from the Selknam, 74 from the Yamana, and 35 from the Alakaluf ethnic groups), it was possible to identify the Selknam men with frontal deformation and the Yamana women with flattening of the parietal regions at the height of the bregma. CONCLUSIONS: From there on with the information provided by the graphical representation of the populations in the most informative dimensions and the homogeneity contrast between sexes, we related the frontal deformation in Selknam men to dragging firewood, vegetable matter, domestic utensils, and heavy pieces of meat from the hunt. On the other hand, the flattening of the parietal areas at the height of the bregma in Yamana women is related to loading and transporting vegetables and animals in baskets or leather sacks.

Sex prediction from the femur and hip bone using a sample of CT images from a Spanish population.

Sex estimation and the analysis of sexual dimorphism is an essential part of forensic and archaeological studies of skeletons. However, osteologists often have to rely on single measurements, such as femoral head diameters, to estimate sex, especially when skeletons are incomplete. We have obtained a sex-prediction model based on CT images by applying the logistic regression technique to the measurements obtained for the proximal femoral epiphyses and coxal. Nine variables for 114 Spaniards (58 females and 56 males) of known age and sex from a region close to Madrid have been studied. The prediction equation obtained using these nine variables correctly classifies 99.1 % of these individuals. Reducing the equation to the three most explanatory variables (VDH, HDH and MIB) resulted in the correct classification of 98.3 %. These findings suggest that this procedure is highly effective for sex prediction. However, a lack of expertise may produce biases in the measurements obtained from CT images. Moreover, these equations are only most effective for the population for which they were calculated as human growth and body size are sensitive to nutritional variations, environmental stress and the so-called secular trend.

Inference with viral quasispecies diversity indices: clonal and NGS approaches.

Given the inherent dynamics of a viral quasispecies, we are often interested in the comparison of diversity indices of sequential samples of a patient, or in the comparison of diversity indices of virus in groups of patients in a treated versus control design. It is then important to make sure that the diversity measures from each sample may be compared with no bias and within a consistent statistical framework. In the present report, we review some indices often used as measures for viral quasispecies complexity and provide means for statistical inference, applying procedures taken from the ecology field. In particular, we examine the Shannon entropy and the mutation frequency, and we discuss the appropriateness of different normalization methods of the Shannon entropy found in the literature. By taking amplicons ultra-deep pyrosequencing (UDPS) raw data as a surrogate of a real hepatitis C virus viral population, we study through in-silico sampling the statistical properties of these indices under two methods of viral quasispecies sampling, classical cloning followed by Sanger sequencing (CCSS) and next-generation sequencing (NGS) such as UDPS. We propose solutions specific to each of the two sampling methods-CCSS and NGS-to guarantee statistically conforming conclusions as free of bias as possible. CONTACT: josep.gregori@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Comparison of lists of genes based on functional profiles.

BACKGROUND: How to compare studies on the basis of their biological significance is a problem of central importance in high-throughput genomics. Many methods for performing such comparisons are based on the information in databases of functional annotation, such as those that form the Gene Ontology (GO). Typically, they consist of analyzing gene annotation frequencies in some pre-specified GO classes, in a class-by-class way, followed by p-value adjustment for multiple testing. Enrichment analysis, where a list of genes is compared against a wider universe of genes, is the most common example. RESULTS: A new global testing procedure and a method incorporating it are presented. Instead of testing separately for each GO class, a single global test for all classes under consideration is performed. The test is based on the distance between the functional profiles, defined as the joint frequencies of annotation in a given set of GO classes. These classes may be chosen at one or more GO levels. The new global test is more powerful and accurate with respect to type I errors than the usual class-by-class approach. When applied to some real datasets, the results suggest that the method may also provide useful information that complements the tests performed using a class-by-class approach if gene counts are sparse in some classes. An R library, goProfiles, implements these methods and is available from Bioconductor, http://bioconductor.org/packages/release/bioc/html/goProfiles.html. CONCLUSIONS: The method provides an inferential basis for deciding whether two lists are functionally different. For global comparisons it is preferable to the global chi-square test of homogeneity. Furthermore, it may provide additional information if used in conjunction with class-by-class methods.

Inferential clustering approach for microarray experiments with replicated measurements.

Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a general clustering approach based on the confidence interval inferential methodology, which is applied to gene expression data of microarray experiments. Emphasis is placed on data with low replication (three or five replicates). The proposed method makes more efficient use of the measured data and avoids the subjective choice of a dissimilarity measure. This new methodology, when applied to real data, provides an easy-to-use bioinformatics solution for the cluster analysis of microarray experiments with replicates (see the Appendix). Even though the method is presented under the framework of microarray experiments, it is a general algorithm that can be used to identify clusters in any situation. The method's performance is evaluated using simulated and publicly available data set. Our results also clearly show that our method is not an extension of the conventional clustering method based on correlation or euclidean distance.

Accuracy in the estimation of quantitative minimal area from the diversity/area curve.

The problem of representativity is fundamental in ecological studies. A qualitative minimal area that gives a good representation of species pool [C.M. Bouderesque, Methodes d'etude qualitative et quantitative du benthos (en particulier du phytobenthos), Tethys 3(1) (1971) 79] can be discerned from a quantitative minimal area which reflects the structural complexity of community [F.X. Niell, Sobre la biologia de Ascophyllum nosodum (L.) Le Jolis en Galicia, Invest. Pesq. 43 (1979) 501]. This suggests that the populational diversity can be considered as the value of the horizontal asymptote corresponding to the curve sample diversity/biomass [F.X. Niell, Les applications de l'index de Shannon a l'etude de la vegetation interdidale, Soc. Phycol. Fr. Bull. 19 (1974) 238]. In this study we develop a expression to determine minimal areas and use it to obtain certain information about the community structure based on diversity/area curve graphs. This expression is based on the functional relationship between the expected value of the diversity and the sample size used to estimate it. In order to establish the quality of the estimation process, we obtained the confidence intervals as a particularization of the functional (h-phi)-entropies proposed in [M. Salicru, M.L. Menendez, D. Morales, L. Pardo, Asymptotic distribution of (h,phi)-entropies, Commun. Stat. (Theory Methods) 22 (7) (1993) 2015]. As an example used to demonstrate the possibilities of this method, and only for illustrative purposes, data about a study on the rocky intertidal seawed populations in the Ria of Vigo (N.W. Spain) are analyzed [F.X. Niell, Estudios sobre la estructura, dinamica y produccion del Fitobentos intermareal (Facies rocosa) de la Ria de Vigo. Ph.D. Mem. University of Barcelona, Barcelona, 1979].