Microarray time course experiments: finding profiles.

Time course studies with microarray techniques and experimental replicates are very useful in biomedical research. We present, in replicate experiments, an alternative approach to select and cluster genes according to a new measure for association between genes. First, the procedure normalizes and standardizes the expression profile of each gene, and then, identifies scaling parameters that will further minimize the distance between replicates of the same gene. Then, the procedure filters out genes with a flat profile, detects differences between replicates, and separates genes without significant differences from the rest. For this last group of genes, we define a mean profile for each gene and use it to compute the distance between two genes. Next, a hierarchical clustering procedure is proposed, a statistic is computed for each cluster to determine its compactness, and the total number of classes is determined. For the rest of the genes, those with significant differences between replicates, the procedure detects where the differences between replicates lie, and assigns each gene to the best fitting previously identified profile or defines a new profile. We illustrate this new procedure using simulated data and a representative data set arising from a microarray experiment with replication, and report interesting results.

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.

Statistical analysis of post mortem DNA damage-derived miscoding lesions in Neandertal mitochondrial DNA.

BACKGROUND: We have analysed the distribution of post mortem DNA damage derived miscoding lesions from the datasets of seven published Neandertal specimens that have extensive cloned sequence coverage over the mitochondrial DNA (mtDNA) hypervariable region 1 (HVS1). The analysis was restricted to C-->T and G-->A miscoding lesions (the predominant manifestation of post mortem damage) that are seen at a frequency of more than one clone among sequences from a single PCR, but do not represent the true endogenous sequence. FINDINGS: The data indicates an extreme bias towards C-->T over G-->A miscoding lesions (observed ratio of 67:2 compared to an expected ratio of 7:2), implying that the mtDNA Light strand molecule suffers proportionally more damage-derived miscoding lesions than the Heavy strand. CONCLUSION: The clustering of Cs in the Light strand as opposed to the singleton pattern of Cs in the Heavy strand could explain the observed bias, a phenomenon that could be further tested with non-PCR based approaches. The characterization of the HVS1 hotspots will be of use to future Neandertal mtDNA studies, with specific regards to assessing the authenticity of new positions previously unknown to be polymorphic.

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].

SCHIP: statistics for chromosome interphase positioning based on interchange data.

MOTIVATION: The position of chromosomes in the interphase nucleus is believed to be associated with a number of biological processes. Here, we present a web-based application that helps analyze the relative position of chromosomes during interphase in human cells, based on observed radiogenic chromosome aberrations. The inputs of the program are a table of yields of pairwise chromosome interchanges and a proposed chromosome geometric cluster. Each can either be uploaded or selected from provided datasets. The main outputs are P-values for the proposed chromosome clusters. SCHIP is designed to be used by a number of scientific communities interested in nuclear architecture, including cancer and cell biologists, radiation biologists and mathematical/computational biologists.