Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2007 Nov 15:8:447.
doi: 10.1186/1471-2105-8-447.

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

Kellie J Archer  1 , Catherine I Dumur, G Scott Taylor, Michael D Chaplin, Anthony Guiseppi-Elie, Geraldine Grant, Andrea Ferreira-Gonzalez, Carleton T Garrett
Affiliations
Comparative Study

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

Kellie J Archer et al. BMC Bioinformatics. .

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Affymetrix. Pairwise scatterplots and Pearson's correlation for Affymetrix GeneChips (MAS5 summaries) restricted to the 1,288 genes in common among the three platforms.
Figure 2
Figure 2
C3B. Pairwise scatterplots and Pearson's correlationfor C3B arrays restricted to the 1,288 genes in common among the three platforms.
Figure 3
Figure 3
GMU. Pairwise scatterplots and Pearson's correlation for GMU arrays restricted to the 1,288 genes in common among the three platforms.
Figure 4
Figure 4
Histogram of log2 absolute tag counts from SAGE. Histogram of log2 absolute tag counts from Serial Analysis of Gene Expression using the Stratagene Total Human RNA for the 14000 unique tags. Data available from GEO Accession #GSM1734.
Figure 5
Figure 5
Histogram of log2 average Affymetrix MAS5 signal. Histogram of log2 average Affymetrix MAS5 signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.
Figure 6
Figure 6
Histogram of log2 average C3B signal. Histogram of log2 average C3B signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.
Figure 7
Figure 7
Histogram of log2 average GMU signal. Histogram of log2 average GMU signal for the Stratagene Total Human RNA using the 1,288 genes in common among the three platforms.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Hwang KB, Kong SW, Greenberg SA, Park PJ. Combining gene expression data from different generations of oligonucleotide arrays. BMC Bioinformatics. 2004;5:159. doi: 10.1186/1471-2105-5-159. - DOI - PMC - PubMed
    1. Nimgaonkar A, Sanoudou D, Butte AJ, Haslett JN, Kunkel LM, Beggs AH, Kohane IS. Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics. 2003;4:27. doi: 10.1186/1471-2105-4-27. - DOI - PMC - PubMed
    1. Yue H, Eastman PS, Wang BB, Minor J, Doctolero MH, Nuttall RL, Stack R, Becker JW, Montgomery JR, Vainer M, Johnston R. An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression. Nucleic Acids Research. 2001;29:e41. doi: 10.1093/nar/29.8.e41. - DOI - PMC - PubMed
    1. Yang IV, Chen E, Hasseman JP, Liang W, Frank BC, Wang S, Sharov V, Saeed AI, White J, Li J, Lee NH, Yeatman TJ, Quackenbush J. Within the fold: assessing differential expression measures and reproducibility in microarray assays. Genome Biology. 2002;3:research0062.1–0062.12. doi: 10.1186/gb-2002-3-11-research0062. - DOI - PMC - PubMed
    1. Kuo W, Jenssen T, Butte A, Ohno-Machado L, Kohane I. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics. 2002;18:405–412. doi: 10.1093/bioinformatics/18.3.405. - DOI - PubMed

Publication types

MeSH terms