Literature DB >> 15846361

Multiple-laboratory comparison of microarray platforms.

Rafael A Irizarry1, Daniel Warren, Forrest Spencer, Irene F Kim, Shyam Biswal, Bryan C Frank, Edward Gabrielson, Joe G N Garcia, Joel Geoghegan, Gregory Germino, Constance Griffin, Sara C Hilmer, Eric Hoffman, Anne E Jedlicka, Ernest Kawasaki, Francisco Martínez-Murillo, Laura Morsberger, Hannah Lee, David Petersen, John Quackenbush, Alan Scott, Michael Wilson, Yanqin Yang, Shui Qing Ye, Wayne Yu.   

Abstract

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Mesh:

Year:  2005        PMID: 15846361     DOI: 10.1038/nmeth756

Source DB:  PubMed          Journal:  Nat Methods        ISSN: 1548-7091            Impact factor:   28.547


  341 in total

1.  Module-based prediction approach for robust inter-study predictions in microarray data.

Authors:  Zhibao Mi; Kui Shen; Nan Song; Chunrong Cheng; Chi Song; Naftali Kaminski; George C Tseng
Journal:  Bioinformatics       Date:  2010-08-17       Impact factor: 6.937

Review 2.  Dynamic changes in gene expression during human early embryo development: from fundamental aspects to clinical applications.

Authors:  Said Assou; Imène Boumela; Delphine Haouzi; Tal Anahory; Hervé Dechaud; John De Vos; Samir Hamamah
Journal:  Hum Reprod Update       Date:  2010-08-17       Impact factor: 15.610

3.  Power of deep sequencing and agilent microarray for gene expression profiling study.

Authors:  Lin Feng; Hang Liu; Yu Liu; Zhike Lu; Guangwu Guo; Suping Guo; Hongwei Zheng; Yanning Gao; Shujun Cheng; Jian Wang; Kaitai Zhang; Yong Zhang
Journal:  Mol Biotechnol       Date:  2010-06       Impact factor: 2.695

Review 4.  Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation.

Authors:  Björn Reinius; Rickard Sandberg
Journal:  Nat Rev Genet       Date:  2015-10-07       Impact factor: 53.242

5.  Covariance adjustment for batch effect in gene expression data.

Authors:  Jung Ae Lee; Kevin K Dobbin; Jeongyoun Ahn
Journal:  Stat Med       Date:  2014-03-28       Impact factor: 2.373

6.  Conceptual aspects of large meta-analyses with publicly available microarray data: a case study in oncology.

Authors:  Markus Schmidberger; Sabine Lennert; Ulrich Mansmann
Journal:  Bioinform Biol Insights       Date:  2011-01-23

Review 7.  Systems approaches to molecular cancer diagnostics.

Authors:  Shuyi Ma; Cory C Funk; Nathan D Price
Journal:  Discov Med       Date:  2010-12       Impact factor: 2.970

Review 8.  Standards affecting the consistency of gene expression arrays in clinical applications.

Authors:  Steven A Enkemann
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-03-23       Impact factor: 4.254

Review 9.  Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments.

Authors:  Yulan Liang; Arpad Kelemen
Journal:  Funct Integr Genomics       Date:  2005-11-15       Impact factor: 3.410

10.  RNA expression profiling at the single molecule level.

Authors:  Jan Hesse; Jaroslaw Jacak; Maria Kasper; Gerhard Regl; Thomas Eichberger; Martina Winklmayr; Fritz Aberger; Max Sonnleitner; Robert Schlapak; Stefan Howorka; Leila Muresan; Anna-Maria Frischauf; Gerhard J Schütz
Journal:  Genome Res       Date:  2006-06-29       Impact factor: 9.043

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.