TITLE

A new efficient statistical test for detecting variability in the gene expression data

AUTHOR(S)
Mathur, Sunil; Dolo, Samuel
PUB. DATE
August 2008
SOURCE
Statistical Methods in Medical Research;Aug2008, Vol. 17 Issue 4, p405
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
DNA microarray technology allows researchers to monitor the expressions of thousands of genes under different conditions. The detection of differential gene expression under two different conditions is very important in microarray studies. Microarray experiments are multi-step procedures and each step is a potential source of variance. This makes the measurement of variability difficult because approach based on gene-by-gene estimation of variance will have few degrees of freedom. It is highly possible that the assumption of equal variance for all the expression levels may not hold. Also, the assumption of normality of gene expressions may not hold. Thus it is essential to have a statistical procedure which is not based on the normality assumption and also it can detect genes with differential variance efficiently. The detection of differential gene expression variance will allow us to identify experimental variables that affect different biological processes and accuracy of DNA microarray measurements. In this article, a new nonparametric test for scale is developed based on the arctangent of the ratio of two expression levels. Most of the tests available in literature require the assumption of normal distribution, which makes them inapplicable in many situations, and it is also hard to verify the suitability of the normal distribution assumption for the given data set. The proposed test does not require the assumption of the distribution for the underlying population and hence makes it more practical and widely applicable. The asymptotic relative efficiency is calculated under different distributions, which show that the proposed test is very powerful when the assumption of normality breaks down. Monte Carlo simulation studies are performed to compare the power of the proposed test with some of the existing procedures. It is found that the proposed test is more powerful than commonly used tests under almost all the distributions considered in the study. A microarray data is used to illustrate the working of the proposed test. Results indicate that the proposed test is very powerful in detecting the smallest change in differential expression variance with high degree of confidence than some of its competitors.
ACCESSION #
33764140

 

Related Articles

  • Incorporation of biological knowledge into distance for clustering genes. Boratyn, Grzegorz M.; Datta, Susmita; Datta, Somnath // Bioinformation;2007, Vol. 1 Issue 10, p396 

    In this paper we propose a data based algorithm to marry existing biological knowledge (e.g., functional annotations of genes) with experimental data (gene expression profiles) in creating an overall dissimilarity that can be used with any clustering algorithm that uses a general dissimilarity...

  • Assessment Of Microarray Technology Using MAQC Samples. Lucas, Anne Bergstrom; D'Andrade, Petula; Chan, Athene; Wong, Alex; Xiangyang Zhou; Corson, John; Fulmer-Smentek, Stephanie // Bioscience Technology;Jul2007, Vol. 32 Issue 7, p32 

    The article focuses on microarray technology. Microarray performance is generally achieved through balancing factors such as sensitivity, accuracy and reproducibility. Relative accuracy of microarray platforms can be assessed by comparison to gene expression measurements collected by alternative...

  • Distributional fold change test -- a statistical approach for detecting differential expression in microarray experiments. Farztdinov, Vadim; McDyer, Fionnuala // Algorithms for Molecular Biology;2012, Vol. 7 Issue 1, p29 

    Background: Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to...

  • A model of binding on DNA microarrays: understanding the combined effect of probe synthesis failure, cross-hybridization, DNA fragmentation and other experimental details of affymetrix arrays. Jakubek, Yasminka A.; Cutler, David J. // BMC Genomics;2012, Vol. 13 Issue 1, p737 

    Background: DNA microarrays are used both for research and for diagnostics. In research, Affymetrix arrays are commonly used for genome wide association studies, resequencing, and for gene expression analysis. These arrays provide large amounts of data. This data is analyzed using statistical...

  • Hypoxic transcription gene profiles under the modulation of nitric oxide in nuclear run on-microarray and proteomics. Igwe, Emeka I.; Essler, Silke; Al-Furoukh, Natalie; Dehne, Nathalie; Brüne, Bernhard // BMC Genomics;2009, Vol. 10, p408 

    Background: Microarray analysis still is a powerful tool to identify new components of the transcriptosome. It helps to increase the knowledge of targets triggered by stress conditions such as hypoxia and nitric oxide. However, analysis of transcriptional regulatory events remain elusive due to...

  • Between-groups within-gene heterogeneity of residual variances in microarray gene expression data. Casellas, Joaquin; Varona, Luis // BMC Genomics;2008, Vol. 9, Special section p1 

    Background: The analysis of microarray gene expression data typically tries to identify differential gene expression patterns in terms of differences of the mathematical expectation between groups of arrays (e.g. treatments or biological conditions). Nevertheless, the differential expression...

  • Improving the power for detecting overlapping genes from multiple DNA microarray-derived gene lists. Xutao Deng; Jun Xu; Wang, Charles // BMC Bioinformatics;2008 Supplement 6, Vol. 9, Special section p1 

    Background: In DNA microarray gene expression profiling studies, a fundamental task is to extract statistically significant genes that meet certain research hypothesis. Currently, Venn diagram is a frequently used method for identifying overlapping genes that meet the investigator's research...

  • Submission of Microarray Data to Public Repositories. BaIi, Catherine A.; Brazma, Alvis; Causton, Helen; Chervitz, Steve; Edgar, Ron; Hingamp, Pascal; Matese, John C.; Parkinson, Helen; Quackenbush, John; Ringwald, Martin; Sansone, Susanna-assunta; Sherlock, Gavin; Spellman, Paul; Stoeckert, Chris; Tateno, Yoshio; Taylor, Ronald; White, Joseph; Winegarden, Neil // PLoS Biology;Sep2004, Vol. 2 Issue 9, p1276 

    This article discusses the submission of microarray data to public repositories. A fundamental principle guiding the publication of scientific results is that the data supporting any scholarly work must be made hilly available to the research community, in a form that allows the basic...

  • Orymold: ontology based gene expression data integration and analysis tool applied to rice. Mercadé, Jaume; Espinosa, Antonio; Adsuara, José-Enrique; Adrados, Rosa; Segura, Jordi; Maes, Tamara // BMC Bioinformatics;2009, Vol. 10, Special section p1 

    Background: Integration and exploration of data obtained from genome wide monitoring technologies has become a major challenge for many bioinformaticists and biologists due to its heterogeneity and high dimensionality. A widely accepted approach to solve these issues has been the creation and...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics