Abstract
BackgroundMicroarrays have been used extensively to analyze the expression profiles for thousands of genes in parallel. Most of the widely used methods for analyzing Affymetrix Genechip microarray data, including RMA, GCRMA and Model Based Expression Index (MBEI), summarize probe signal intensity data to generate a single measure of expression for each transcript on the array. In contrast, other methods are applied directly to probe intensities, negating the need for a summarization step.ResultsIn this study, we used the Affymetrix rat genome Genechip to explore variability in probe response patterns within transcripts. We considered a number of possible sources of variability in probe sets including probe location within the transcript, middle base pair of the probe sequence, probe overlap, sequence homology and affinity. Although affinity, middle base pair and probe location effects may be seen at the gross array level, these factors only account for a small proportion of the variation observed at the gene level. A BLAST search and the presence of probe by treatment interactions for selected differentially expressed genes showed high sequence homology for many probes to non-target genes.ConclusionWe suggest that examination and modeling of probe level intensities can be used to guide researchers in refining their conclusions regarding differentially expressed genes. We discuss implications for probe sequence selection for confirmatory analysis using real time PCR.
Highlights
Microarrays have been used extensively to analyze the expression profiles for thousands of genes in parallel
The signal intensity of probe sets for each gene should be related to the abundance of the transcript, which can be used to quantify the level of gene expression
HFiigstuorgera5ms of BLAST results conducted on 8 selected genes Histograms of BLAST results conducted on 8 selected genes
Summary
Microarrays have been used extensively to analyze the expression profiles for thousands of genes in parallel. Most of the widely used methods for analyzing Affymetrix Genechip microarray data, including RMA, GCRMA and Model Based Expression Index (MBEI), summarize probe signal intensity data to generate a single measure of expression for each transcript on the array. Microarray technology is a high-throughput method for studying the expression of thousands of genes simultaneously. Most popular methods for analyzing Affymetrix Genechip microarray data include background correction, normalization and summarization steps. RMA [1] uses a model-based background correction, quantile normalization, and median polish summarization. These methods result in a robust and interpreted measure of expression for each probe set on an array. Subsequent tests for differential expression based on these methods have lower computing costs than probe level linear models
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