Exploring the use of internal and externalcontrols for assessing microarray technical performance

Abstract

Background: The maturing of gene expression microarray technology and interest in the use of microarray-based applications for clinical and diagnostic applications calls for quantitative measures of quality. This manuscript presents a retrospective study characterizing several approaches to assess technical performance of microarray data measured on the Affymetrix GeneChip platform, including whole-array metrics and information from a standard mixture of external spike-in and endogenous internal controls. Spike-in controls were found to carry the same information about technical performance as whole-array metrics and endogenous "housekeeping" genes. These results support the use of spike-in controls as general tools for performance assessment across time, experimenters and array batches, suggesting that they have potential for comparison of microarray data generated across species using different technologies.

Results: A layered PCA modeling methodology that uses data from a number of classes of controls (spike-in hybridization, spike-in polyA+, internal RNA degradation, endogenous or "housekeeping genes") was used for the assessment of microarray data quality. The controls provide information on multiple stages of the experimental protocol (e.g., hybridization, RNA amplification). External spike-in, hybridization and RNA labeling controls provide information related to both assay and hybridization performance whereas internal endogenous controls provide quality information on the biological sample. We find that the variance of the data generated from the external and internal controls carries critical information about technical performance; the PCA dissection of this variance is consistent with whole-array quality assessment based on a number of quality assurance/quality control (QA/QC) metrics.

Conclusions: These results provide support for the use of both external and internal RNA control data to assess the technical quality of microarray experiments. The observed consistency amongst the information carried by internal and external controls and whole-array quality measures offers promise for rationally-designed control standards for routine performance monitoring of multiplexed measurement platforms.

Figure 1

Overview of the classes of controls (internal and external) used within a microarray experiment together with a schematic illustrating the addition of external controls at different steps during sample processing. (a) Overview of the classes of controls (internal and external) used within a microarray experiment. Each class reports on variability originating at multiple stages. (b) Schematic protocol showing the addition of external spike-in polyA+ and hybridization controls at different steps during sample processing.

Figure 2

Mean/SD plots of the RMA values for all probeset data pairs for the 137 hybridizations of the rat dataset (a) without normalization, (b) with quantile normalization and (c) with 75% percentile normalization. The signal level scale is shifted by 2⁸ for the 75% percentile normalization data (c). Separate symbols denote probeset data pairs (mean, SD) for the spiked-in hybridization (▲) and polyA+ (▲) controls and for the cRNA degradation (●) and endogenous/housekeeping (○) internal controls. Non-control (background) probesets and the moving mean derived from them are denoted with gray-filled symbols, (•) and (•), respectively. Select spiked-in polyA+ control and RNA degradation probesets are labeled according to the abbreviations in Additional File 1: Supplemental Table S2.

Figure 3

1-D PCA score plots for the principal components (PC 1, PC 2 and PC 3) for the external spike-in hybridization controls of the rat dataset. Symbols are color-coded according to the date of hybridization (A - M; see legend) and data from single arrays are overlaid on box plots that summarize the data in each date class. A subset of data points are labeled with both the date class abbreviation (A - M) and the hybridization number (1-137).

Figure 4

Unfolded 3-D PCA scores plot (PC 2 × PC 3 × PC 4) for the external spike-in polyA+ controls subset of the rat dataset. Symbols represent the date class (A - M; see legend).

Figure 5

Unfolded 3-D PCA scores plot (PC 1 × PC 2 × PC 3) for the internal cRNA degradation controls subset of the single Rat dataset. Symbols as Figure 4.

Figure 6

Two sets of unfolded 3-D PCA scores plot (PC 1 × PC 2 × PC 3 and PC 4 × PC 5 × PC 6) for the endogenous controls from the rat dataset. Symbols as Figures 4 and 5.