Assessment of differential gene expression in human peripheral nerve injury

Yuanyuan Xiao; Segal, Mark R.; Rabert, Douglas; Ahn, Andrew H.; Anand, Praveen; Sangameswaran, Lakshmi; Donglei Hu; Hunt, C. Anthony
January 2002
BMC Genomics;2002, Vol. 3, p28
Academic Journal
Background: Microarray technology is a powerful methodology for identifying differentially expressed genes. However, when thousands of genes in a microarray data set are evaluated simultaneously by fold changes and significance tests, the probability of detecting false positives rises sharply. In this first microarray study of brachial plexus injury, we applied and compared the performance of two recently proposed algorithms for tackling this multiple testing problem, Significance Analysis of Microarrays (SAM) and Westfall and Young step down adjusted p values, as well as t-statistics and Welch statistics, in specifying differential gene expression under different biological states. Results: Using SAM based on t statistics, we identified 73 significant genes, which fall into different functional categories, such as cytokines / neurotrophin, myelin function and signal transduction. Interestingly, all but one gene were down-regulated in the patients. Using Welch statistics in conjunction with SAM, we identified an additional set of up-regulated genes, several of which are engaged in transcription and translation regulation. In contrast, the Westfall and Young algorithm identified only one gene using a conventional significance level of 0.05. Conclusion: In coping with multiple testing problems, Family-wise type I error rate (FWER) and false discovery rate (FDR) are different expressions of Type I error rates. The Westfall and Young algorithm controls FWER. In the context of this microarray study, it is, seemingly, too conservative. In contrast, SAM, by controlling FDR, provides a promising alternative. In this instance, genes selected by SAM were shown to be biologically meaningful.


Related Articles

  • Analysis of large-scale gene expression data. Sherlock, Gavin // Briefings in Bioinformatics;Dec2001, Vol. 2 Issue 4, p38 

    DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray...

  • AffyMiner: mining differentially expressed genes and biological knowledge in GeneChip microarray data. Guoqing Lu; Nguyen, The V.; Yuannan Xia; Fromm, Michael // BMC Bioinformatics;2006 Supplement 4, Vol. 7, pS26 

    Background: DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously. With the advance of microarray technology, the challenge issue becomes how to analyze a large amount of microarray data and make biological sense of them. Affymetrix...

  • Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR -- how well do they correlate? Dallas, Peter B; Gottardo, Nicholas G; Firth, Martin J; Beesley, Alex H; Hoffmann, Katrin; Terry, Philippa A; Freitas, Joseph R; Boag, Joanne M; Cummings, Aaron J; Kees, Ursula R // BMC Genomics;2005, Vol. 6, p59 

    Background: The use of microarray technology to assess gene expression levels is now widespread in biology. The validation of microarray results using independent mRNA quantitation techniques remains a desirable element of any microarray experiment. To facilitate the comparison of microarray...

  • A novel parametric approach to mine gene regulatory relationship from microarray datasets. Wanlin Liu; Dong Li; Qijun Liu; Yunping Zhu; Fuchu He // BMC Bioinformatics;Jan2010 Supplement 11, Vol. 11, p1 

    Background: Microarray has been widely used to measure the gene expression level on the genome scale in the current decade. Many algorithms have been developed to reconstruct gene regulatory networks based on microarray data. Unfortunately, most of these models and algorithms focus on global...

  • Using a Genetic Algorithm and a Perceptron for Feature Selection and Supervised Class Learning in DNA Microarray Data. Michal Karzynski; Álvaro Mateos; Javier Herrero; Joaquín Dopazo // Artificial Intelligence Review;Oct2003, Vol. 20 Issue 1/2, p39 

    Class prediction and feature selection is key in the context of diagnostic applications of DNA microarrays. Microarray data is noisy and typically composed of a low number of samples and a large number of genes. Perceptrons can constitute an efficient tool for accurate classification of...

  • Functional genomics via multiscale analysis: application to gene expression and ChIP-on-chip data. Gilad Lerman; Joseph McQuown; Alexandre Blais; Brian D. Dynlacht; Guangliang Chen; Bud Mishra // Bioinformatics;Feb2007, Vol. 23 Issue 3, p314 

    We present a fast, versatile and adaptive-multiscale algorithm for analyzing a wide-variety of DNA microarray data. Its primary application is in normalization of array data as well as subsequent identification of ‘enriched targets’, e.g. differentially expressed genes in expression...

  • Clustering gene expression data with a penalized graph-based metric. Bayá, Ariel E.; Granitto, Pablo M. // BMC Bioinformatics;2011, Vol. 12 Issue 1, p1 

    Background: The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary...

  • Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity. Kadota, Koji; Nakai, Yuji; Shimizu, Kentaro // Algorithms for Molecular Biology;2009, Vol. 4, p1 

    Background: To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expressionlevel measurements and a way of ranking genes to obtain the most plausible candidates. We recently...

  • Two-stage testing in microarray analysis: what is gained? Allison, David B.; Coffey, Christopher S. // Journals of Gerontology Series A: Biological Sciences & Medical ;May2002, Vol. 57 Issue 5, pB189 

    Microarray technology for gene expression studies offers powerful new technology for understanding changes in gene expression as a function of other observable or manipulable variables. However, microarrays also pose a number of new challenges. One of the most prominent of these is the...

  • Cancer cells, chemotherapy and gene clusters. Pinkel, Daniel // Nature Genetics;Mar2000, Vol. 24 Issue 3, p208 

    Reports on the combination of large-scale microarray-based gene expression analyses. response to chemotherapeutic agents in a panel of cancer lines; Insights on the state of the cells from the cellular levels of RNA transcripts; Classification of cancer cell lines according to their tissue of...


Read the Article


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

Try another library?
Sign out of this library

Other Topics