Optimization of cDNA microarrays procedures using criteria that do not rely on external standards

Abstract

Background: The measurement of gene expression using microarray technology is a complicated process in which a large number of factors can be varied. Due to the lack of standard calibration samples such as are used in traditional chemical analysis it may be a problem to evaluate whether changes done to the microarray procedure actually improve the identification of truly differentially expressed genes. The purpose of the present work is to report the optimization of several steps in the microarray process both in laboratory practices and in data processing using criteria that do not rely on external standards.

Results: We performed a cDNA microarry experiment including RNA from samples with high expected differential gene expression termed "high contrasts" (rat cell lines AR42J and NRK52E) compared to self-self hybridization, and optimized a pipeline to maximize the number of genes found to be differentially expressed in the "high contrasts" RNA samples by estimating the false discovery rate (FDR) using a null distribution obtained from the self-self experiment. The proposed high-contrast versus self-self method (HCSSM) requires only four microarrays per evaluation. The effects of blocking reagent dose, filtering, and background corrections methodologies were investigated. In our experiments a dose of 250 ng LNA (locked nucleic acid) dT blocker, no background correction and weight based filtering gave the largest number of differentially expressed genes. The choice of background correction method had a stronger impact on the estimated number of differentially expressed genes than the choice of filtering method. Cross platform microarray (Illumina) analysis was used to validate that the increase in the number of differentially expressed genes found by HCSSM was real.

Conclusion: The results show that HCSSM can be a useful and simple approach to optimize microarray procedures without including external standards. Our optimizing method is highly applicable to both long oligo-probe microarrays which have become commonly used for well characterized organisms such as man, mouse and rat, as well as to cDNA microarrays which are still of importance for organisms with incomplete genome sequence information such as many bacteria, plants and fish.

Figure 1

The High Contrast versus Self-Self method (HCSSM). The figure illustrates how the false discovery rate is determined in HCSSM. For a chosen T cut-off, the genes with T-scores larger than the cut-off are declared significant. The false discovery rate is then determined by dividing the number of genes deemed significant in the self-self experiment by the number of genes deemed significant in the high contrast experiment. For example, if for a T-cut-off six genes are declared significant from the self-self experiment and 127 genes are declared significant from the high contrast experiment the false discovery rate will be 6/127 ~0.05. If a specific false discovery rate is wanted (often 0.05 or 0.01), the T cut-off can be adjusted to obtain it.

Figure 2

Graphical representation of experimental design of the microarray experiment. The nodes correspond to RNA from samples with high expected differential gene expression (rat cell lines NRK52E and AR42J) compared to self-self hybridization (rat cell line AR42J). The samples were hybridized to rat 15 k cDNA duplicates under six different blocking conditions including no blocker, 1000 ng poly(dA)_40–60, and 25 to 1000 ng LNA dT blocker. Dye-swap and self versus self were performed for all blocking conditions (total of 24 hybridizations). Green-labelled samples are placed at the tail and red labelled samples at the head of the arrows.

Figure 3

Effect of choice and dose of dT blocker on the number of differentially expressed genes. The figure shows data analysed with weighted filtration and no background correction (see Methods section).

Figure 4

Effects of background correction and level of filtration on the number of differentially expressed genes estimated. The figure shows data from hybridization with 250 ng LNA blocker added.

Figure 5

Comparison of gene lists generated with different background correction methods. The left figure illustrates the number of differentially expressed genes estimated with no background correction (A) and dampened Edwards background correction (B). The Venn diagram shows overlap of differentially expressed genes estimated with no background correction (blue) and dampened Edwards (grey). All data are from hybridization with 250 ng LNA blocker added and weight based filtering.

Figure 6

Cross platform microarray analysis. The figure shows ratios (log2 based) from 553 genes comparable between the cDNA and the Illumina platforms. Red dots: genes significantly up-regulated on the Illumina platform. Green dots: genes significantly down-regulated on the Illumina platform. Black dots: genes not identified as significantly different between the cell lines (AR42J versus NRK52E) on the Illumina platform. All the genes were identified as differentially expressed on the cDNA platform, and the concordance between cDNA and Illumia is 72 %.