Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

Area of Science:

• Bioinformatics
• Genomics
• Gene Expression Analysis

Background:

• Affymetrix GeneChip microarrays are widely used for gene expression profiling.
• Preprocessing algorithms aim to remove noise and combine probe data for accurate transcript representation.
• Quantitative reverse-transcription PCR (qRT-PCR) serves as a benchmark for validating microarray results.

Purpose of the Study:

• To comprehensively analyze the agreement between Affymetrix GeneChip and qRT-PCR results.
• To evaluate the impact of different preprocessing algorithms, filtering methods, and annotation versions on data accuracy.
• To provide recommendations for optimal analysis strategies in gene expression studies.

Main Methods:

• Analysis of agreement between Affymetrix GeneChip and qRT-PCR using linear and rank correlation coefficients.
• Evaluation of six preprocessing algorithms: MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm.
• Assessment of filtering by fraction Present calls, two mapping procedures, and annotation release dates.

Main Results:

• Filtering by fraction Present calls consistently increased correlations across all tested algorithms.
• PM-MM methods improved fold change correlations, while PM-only methods were superior for differential expression detection.
• Using updated genome annotations significantly improved results for both analysis types.

Conclusions:

• Filtering by fraction Present calls is recommended for enhancing microarray data accuracy.
• GC-RMA is suggested for differential expression detection, and PLIER for fold change estimation.
• Re-analyzing existing microarray data with updated annotations can yield improved insights.