Application of a correlation correction factor in a microarray cross-platform reproducibility study

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations.

Results

In this paper, three technical replicate microarrays were hybridized to each of three platforms. The three platforms were then analyzed to assess both intra- and cross-platform reproducibility. We present various methods for examining intra-platform reproducibility. We also examine cross-platform reproducibility using Pearson's correlation. Additionally, we previously developed a correction factor for Pearson's correlation which is applicable when X and Y are measured with error. Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.

Conclusion

When estimating cross-platform correlation, it is essential to thoroughly evaluate intra-platform reproducibility as a first step. In addition, since measurement error is present in microarray gene expression data, methods to correct for attenuation are useful in decreasing the bias in cross-platform correlation estimates.

Related collections

Most cited references 61

Record: found
Abstract: not found
Article: not found

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Y. H. Yang (2002)

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.

0 comments Cited 803 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

Daniel Rhodes, Jianjun Yu, K. Shanker … (2004)

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.

0 comments Cited 324 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Multiple-laboratory comparison of microarray platforms.

Rafael A. Irizarry, Daniel P Warren, Forrest Spencer … (2005)

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.

0 comments Cited 251 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central

ISSN (Electronic): 1471-2105

Publication date Collection: 2007

Publication date (Electronic): 15 November 2007

Volume: 8

Page: 447

Affiliations

[1 ]Department of Biostatistics, Virginia Commonwealth University, 730 East Broad St., Richmond, VA, USA

[2 ]Department of Pathology, Virginia Commonwealth University, Richmond, VA, USA

[3 ]Center for Bioelectronics, Biosensors and Biochips, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA

[4 ]Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA

[5 ]Molecular and Microbiological Department, George Mason University, Manassas, VA, USA

Article

Publisher ID: 1471-2105-8-447

DOI: 10.1186/1471-2105-8-447

PMC ID: 2211756

PubMed ID: 18005444

SO-VID: e55d49f5-7300-4732-9a61-0752a9dbadcc

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Application of a correlation correction factor in a microarray cross-platform reproducibility study

Read this article at

Abstract

Background

Results

Conclusion

Related collections

Genetoberfest

Most cited references 61

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

Multiple-laboratory comparison of microarray platforms.

Author and article information

Journal

Affiliations

Article

History

Categories

Comments

Comment on this article

Similar content 201

Cited by 2

Most referenced authors 1,670