cDNA Microarray Experiments (cdna + microarray_experiment)

Distribution by Scientific Domains
Mathematics and Statistics75%

Selected Abstracts

Expression profiling of Wilms tumors reveals new candidate genes for different clinical parameters

INTERNATIONAL JOURNAL OF CANCER, Issue 8 2006
B. Zirn
Abstract Wilms tumor is the most frequent renal neoplasm in children, but our understanding of its genetic basis is still limited. We performed cDNA microarray experiments using 63 primary Wilms tumors with the aim of detecting new candidate genes associated with malignancy grade and tumor progression. All tumors had received preoperative chemotherapy as mandated by the SIOP protocol, which sets this study apart from related approaches in the Unites States that are based on untreated samples. The stratification of expression data according to clinical criteria allowed a rather clear distinction between different subsets of Wilms tumors. Clear-cut differences in expression patterns were discovered between relapse-free as opposed to relapsed tumors and tumors with intermediate risk as opposed to high risk histology. Several differentially expressed genes, e.g.TRIM22, CENPF, MYCN, CTGF, RARRES3 and EZH2, were associated with Wilms tumor progression. For a subset of differentially expressed genes, microarray data were confirmed by real-time RT-PCR on the original set of tumors. Interestingly, we found the retinoic acid pathway to be deregulated at different levels in advanced tumors suggesting that treatment of these tumors with retinoic acid may represent a promising novel therapeutic approach. © 2005 Wiley-Liss, Inc. [source]

Experimental designs for 2-colour cDNA microarray experiments

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 5-6 2006
Nam-Ky Nguyen
Abstract Kerr and Churchill (Biostatistics 2001; 2:183,201) showed how varieties (e.g. type of tissues, drug treatments, etc.) are paired onto arrays by a catalogue of A-optimal incomplete block designs (IBDs) for 6,10 varieties (v), and number of blocks of size 2 between v and . These A-optimal IBDs were obtained by (i) generating all non-isomorphic connected graphs on v vertices using Brendan McKay's, MAKEG program (http://cs.anu.edu.au/people/bdm/nauty/) and (ii) comparing all designs of the same size on the basis of A-optimality to obtain the best ones. In this paper we will give a quick overview on IBDs and describe an algorithmic approach to extend the mentioned catalogue. We aim at IBDs with up to 100 varieties with equal as well as unequal replications. A catalogue of 2007 IBDs is given. We will also extend the concept of even designs in Kerr and Churchill (Biostatistics 2001; 2:183,201) to row-orthogonal designs. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Improved Detection of Differentially Expressed Genes Through Incorporation of Gene Locations

BIOMETRICS, Issue 3 2009
Guanghua Xiao
Summary In determining differential expression in cDNA microarray experiments, the expression level of an individual gene is usually assumed to be independent of the expression levels of other genes, but many recent studies have shown that a gene's expression level tends to be similar to that of its neighbors on a chromosome, and differentially expressed (DE) genes are likely to form clusters of similar transcriptional activity along the chromosome. When modeled as a one-dimensional spatial series, the expression level of genes on the same chromosome frequently exhibit significant spatial correlation, reflecting spatial patterns in transcription. By modeling these spatial correlations, we can obtain improved estimates of transcript levels. Here, we demonstrate the existence of spatial correlations in transcriptional activity in the,Escherichia coli,(E. coli) chromosome across more than 50 experimental conditions. Based on this finding, we propose a hierarchical Bayesian model that borrows information from neighboring genes to improve the estimation of the expression level of a given gene and hence the detection of DE genes. Furthermore, we extend the model to account for the circular structure of,E. coli,chromosome and the intergenetic distance between gene neighbors. The simulation studies and analysis of real data examples in,E. coli,and yeast,Saccharomyces cerevisiae,show that the proposed method outperforms the commonly used significant analysis of microarray (SAM),t -statistic in detecting DE genes. [source]

Nonparametric Inference for Local Extrema with Application to Oligonucleotide Microarray Data in Yeast Genome

BIOMETRICS, Issue 2 2006
Peter X.-K.
Summary Identifying local extrema of expression profiles is one primary objective in some cDNA microarray experiments. To study the replication dynamics of the yeast genome, for example, local peaks of hybridization intensity profiles correspond to putative replication origins. We propose a nonparametric kernel smoothing (NKS) technique to detect local hybridization intensity extrema across chromosomes. The novelty of our approach is that we base our inference procedures on equilibrium points, namely those locations at which the first derivative of the intensity curve is zero. The proposed smoothing technique provides both point and interval estimation for the location of local extrema. Also, this technique can be used to test for the hypothesis of either one or multiple suspected locations being the true equilibrium points. We illustrate the proposed method on a microarray data set from an experiment designed to study the replication origins in the yeast genome, in that the locations of autonomous replication sequence (ARS) elements are identified through the equilibrium points of the smoothed intensity profile curve. Our method found a few ARS elements that were not detected by the current smoothing methods such as the Fourier convolution smoothing. [source]