1,721,101 research outputs found
Analysis of gene expression profiles at chromosomal level
No full textTranscriptional profiling of whole genomes using cDNA or oligonucleotide high-density arrays is becoming increasingly popular among the biomedical research community. Although advances in technology and the rapid rise in microarray data availability are leading to new insight into fundamental biological problems, investigators are still confronted with the major problem of upgrading the information content of regulated gene lists obtained from microarray experiments. Indeed, the efficient exploitation of gene expression databases requires not only computational tools for management, analysis, and functional annotation of primary data, but also integrating lists of modulated genes with of other sources of genomic information, such as gene sequence, locus or structural characteristics. In particular, integration between expression profiles and chromosomal localizations could be effective in detecting gene structural abnormalities such as genomic gains and losses and/or translocations. The aim of the present study is to apply computational tools for mapping transcriptional data at chromosomal level and detecting clusters of regionally modulated genes in cancer specimens.Statistical tests and signal processing procedures are used to integrate expression profiles and gene sequence information and identify peculiar regions of modulated expression. In particular, the method is based on the application of a smoothing, coordinate-dependent function (e.g., cubic splines) to a standard transcriptional specificity statistic (e.g., standard F-statistic), commonly used to detect differentially expressed genes.This computational tool has been tested on different microarray data sets obtained from various human tumor samples (e.g., solid tumors and hematological disorders). In particular, the application of chromosomal level analysis to the transcriptional database presented by Bhattacharjee (Bhattacharjee et al., 2001), Armstrong (Armstrong et al., 2002), and Ross (Ross et al., 2003) allowed the detection of regional signals corresponding to known as well as putative loci with high frequent genomic losses and gains or marking translocation events
A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions
No full textMotivation: The systematic integration of expression profiles and other types of gene information, such as chromosomal localization, ontological annotations and sequence characteristics, still represents a challenge in the gene expression arena. In particular, the analysis of transcriptional data in context of the physical location of genes in a genome appears promising in detecting chromosomal regions with transcriptional imbalances often characterizing cancer.Results: A computational tool named locally adaptive statistical procedure (LAP), which incorporates transcriptional data and structural information for the identification of differentially expressed chromosomal regions, is described. LAP accounts for variations in the distance between genes and in gene density by smoothing standard statistics on gene position before testing the significance of their differential levels of gene expression. The procedure smoothes parameters and computes p-values locally to account for the complex structure of the genome and to more precisely estimate the differential expression of chromosomal regions. The application of LAP to three independent sets of raw expression data allowed identifying differentially expressed regions that are directly involved in known chromosomal aberrations characteristic of tumors
Analysis of un-replicated time-course microarray experiments
No full textSince transcriptional control is the result of complex networks, analyzing dynamical states of gene expression is of paramount importance to detect the multivariate nature of biological mechanisms. Although hundreds of studies fully demonstrated the relevancy of microarrays in describing different physiological conditions, to reconstruct complex interaction pathways it is necessary to analyze the temporal evolution of transcriptional states. However, a robust experimental design for identifying differentially expressed genes over a temporal window would require large amounts of microarrays. Unfortunately, replicates for each time point and experimental condition are not always available, because of cost limitations and/or biological samples scarcity. In addition, common data analysis tools, like ANOVA, require replicates and disregard correlation structure among times. We present a method for the identification of differentially expressed genes in un-replicated time-course experiments. The procedure does not assume any model or distribution function, takes into account the correlation of data, and does not require sample replicates at the various time points, other than the presence of an initial time point for all analyzed conditions. The identification of differentially expressed genes as the result of a system perturbation is formally stated as a hypothesis testing problem in which a defined statistic is used to rank transcripts in order of evidence against the null hypothesis. Specifically, i) data are structured so that measurements are correlated in time, within the same biological condition; ii) the null hypothesis is formulated so that changes in expression levels at different time points are equivalent; iii) time point t0 represents the system before the perturbation. Therefore, modulated genes are detected testing the statistical significance of expression differences between physiological states at each time point, once corrected by the variability at t0, and given an empirical null distribution constructed using permutations. Statistical significance is assessed by the q-value. The method has been tested on time-course microarray experiments aimed at studying the temporal changes of gene expression in: i) skeletal muscle cells treated with a histone deacetylase inhibitor (Iezzi et al., Dev Cell; 2004) and ii) immature mouse dendritic cells (DC) exposed to larval and egg stages of S. mansoni (Trottein et al., J Immunol; 2004). Differentially expressed genes, identified using the proposed algorithm, have been compared with results obtained from ANOVA model and SAM paired test. The biological significance and soundness of selected transcripts was also verified using global functional profiling by means of OntoTools. Results demonstrate that this novel procedure allows the identification of biologically relevant genes using half of the replicates required by standard model-based approaches
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Tracheostomizzati in terapia intensiva: problematiche e percorsi clinico-organizzativi.
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