Three microarray platforms: an analysis of their concordance in profiling gene expression

Abstract

Background: Microarrays for the analysis of gene expression are of three different types: short oligonucleotide (25-30 base), long oligonucleotide (50-80 base), and cDNA (highly variable in length). The short oligonucleotide and cDNA arrays have been the mainstay of expression analysis to date, but long oligonucleotide platforms are gaining in popularity and will probably replace cDNA arrays. As part of a validation study for the long oligonucleotide arrays, we compared and contrasted expression profiles from the three formats, testing RNA from six different cell lines against a universal reference standard.

Results: The three platforms had 6430 genes in common. In general, correlation of gene expression levels across the platforms was good when defined by concordance in the direction of expression difference (upregulation or downregulation), scatter plot analysis, principal component analysis, cell line correlation or quantitative RT-PCR. The overall correlations (r values) between platforms were in the range 0.7 to 0.8, as determined by analysis of scatter plots. When concordance was measured for expression ratios significant at p-values of <0.05 and at expression threshold levels of 1.5 and 2-fold, the agreement among the platforms was very high, ranging from 93% to 100%.

Conclusion: Our results indicate that the long oligonucleotide platform is highly suitable for expression analysis and compares favorably with the cDNA and short oligonucleotide varieties. All three platforms can give similar and reproducible results if the criterion is the direction of change in gene expression and minimal emphasis is placed on the magnitude of change.

Figure 1

Venn diagram with number of genes present in each platform, genes in common between platforms, and genes in common among all three platforms.

Figure 2

a-f. Scatter plot analysis to determine correlation coefficients between and within platforms using Jurkat RNA as an example. Correlations for all cell lines are given in Table 4. (a) Operon versus Incyte (b) Affymetrix versus Incyte (c) Affymetrix versus Operon (d) GEM2 versus GEM2 replicate correlation (e) Operon versus Operon (f) HG-U133A versus HG-U133A

Figure 3

Principal Component Analysis (PCA) of the three microarray platforms and six cell lines using expression of the 3186 genes with signals above background.

Figure 4

a-c. Correlation of correlations of platforms for all cell lines. Correlation values R for each pair of platforms are given in the figures. (a) Operon versus Incyte (b) Affymetrix versus Incyte (c) Affymetrix versus Operon.

Figure 5

a-b. Clustered image maps showing patterns of expression relationship among genes, platforms, and cell lines. The axes were ordered by hierarchical clustering using an uncentered correlation and the average linkage algorithm for 909 genes expressed at a two-fold or greater level in at least two of the six cell lines. (a) Clustering of all 909 genes (b) A subcluster of 41 genes to show correct clustering and congruence of expression values. As indicated by the cluster trees, all three platforms gave essentially the same relationships among the six cell lines.

Figure 6

Quantitative real-time RT-PCR analysis of 12 genes matched for direction of expression relative to the reference RNA for all three platforms. Log2 ratios are given in table below the graph. This example is a comparison between LnCaP and MCF-10A.

Figure 7

a-f. Quantitative RT-PCR analysis of 10 mismatched genes in the six cells lines for all three platforms. (a) MCF10A, (b) LnCaP, (c) OCI-Ly3, (d) Jurkat, (e) SUDHL-6 and (f) L428.