Abstract
High density oligonucleotide arrays have become a standard research tool to monitor the expression of thousands of genes simultaneously. Affymetrix GeneChip arrays are the most popular. They use short oligonucleotides to probe for genes in an RNA sample. However, important challenges remain in estimating expression level from raw hybridization in tensities on the array. In this paper, we deal with the problem of estimating gene expression based on a statistical model. The present method is like Li and Wong model (2001a), but assumes more generality. More precisely, we show how the model introduced by Li and Wong can be generalized to provide new measure of gene expression. Moreover, we provide a comparison between these two models.
Highlights
High density oligonucleotide expression arrays are widely used in many area of biomedical research for measurements of gene expression
Every probe pair is composed of a perfect match P M, a section of the mRNA molecule of interest and a mismatch M M, which is identical to the perfect match probe except for the base in the middle (13th) position
Where I denotes the number of samples and J denotes the number of probe pairs in a probe set. θ is the expression index, ν is a non-specific cross-hybridization term, α is the rate of increase of M M intensity and φ is the additional rate of increase of the P M intensity
Summary
High density oligonucleotide expression arrays are widely used in many area of biomedical research for measurements of gene expression. After RNA samples are prepared, labeled and hybridized with arrays, these are scanned and images are produced and processed to obtain an intensity value for each probe. These intensities, P Mij and M Mij, represent the amount of hybridization for arrays i = 1, ...I and probe pairs j = 1, ..., J for any given probe set. There has been considerable discussion over the appropriate algorithm for constructing single expression estimates based on multiple-probe hybridization. We will show that our model gives an unbiased estimate of the expression index with low variance. We compare how well these methods perform using the spike-in experiment H GU95A described in more details in the same section
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