Comparison of Affymetrix GeneChip expression measures

Irizarry, Rafael A.; Wu, Zhijin; Jaffee, Harris A.

doi:10.1093/bioinformatics/btk046

Cited by 272 publications

(276 citation statements)

References 15 publications

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“…The differences in the signal produced can be attributed to many sources: optical noise, cross-hybridization, dye-related contributions and probe sequence composition. Many algorithms have been developed to attempt to correct for these inconsistencies (Irizarry et al, 2006;Wu and Irizarry, 2005;Zhang et al, 2003). In particular, it has been found that probe sequence composition can significantly affect the intensity of the signal generated from that probe, independent of the concentration of its target.…”

Section: Introductionmentioning

confidence: 99%

Software Note: Using probe secondary structure information to enhance Affymetrix GeneChip background estimates

Gharaibeh

Fodor

Gibas

2007

Computational Biology and Chemistry

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High-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data. A number of statistical techniques have been developed to correct for this background noise. Here, we demonstrate that probe minimum folding energy and structure can be used to enhance a previously existing model for background noise correction. We estimate that probe secondary structure accounts for up to 3% of all variation on Affymetrix microarrays.

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Section: Introductionmentioning

confidence: 99%

Software Note: Using probe secondary structure information to enhance Affymetrix GeneChip background estimates

Gharaibeh

Fodor

Gibas

2007

Computational Biology and Chemistry

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“…The work presented here would not have been possible without the existing public repositories. In particular, the availability of raw data was key as the methods used to process raw data into gene level measurements also contribute to study-to-study variability 17,18 . We hope this trend continues, as we believe it to be necessary for microarray technology to fulfill its promise to help diagnose and treat disease.…”

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confidence: 99%

A gene expression bar code for microarray data

2007

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The ability to measure genome-wide expression holds great promise for characterizing cells and distinguishing diseased from normal tissues. Thus far, microarray technology has only been useful for measuring relative expression between two or more samples, which has handicapped its ability to classify tissue types. This paper presents the first method that can successfully predict tissue type based on data from a single hybridization. A preliminary web-tool is available at http://rafalab.jhsph.edu/barcode/ The high throughput analysis of cells and tissues is revolutionizing biological research. The ability of microarrays to measure thousands of RNA transcripts at one time allows for the characterization of cells and tissues in greater depth than was previously possible, but has not yet led to big advances in diagnosis or treatment. The main reason for this is that feature characteristics, such as probe sequence, can cloud the relationship between observed intensity and actual expression. Although this probe effect is large, it is also very consistent across different hybridizations, which implies that relative measures of expression are substantially more useful than absolute ones 1, 2 . To understand this, consider that when comparing intensities from different hybridizations for the same gene, the probe effect is very similar and cancels out. On the other hand, when comparing intensities for two genes from the same hybridization, the different probe effects can alter the observed differences. For this reason the overwhelming majority of results based on microarray data rely on measures of relative expression: genes are reported to be differentially expressed rather than expressed or unexpressed.Approaches for thresholding noisy data have been successfully used in many applications including microarray studies 3, 4 . We used this as motivation to develop the first method that can accurately demarcate expressed from unexpressed genes and therefore defines a unique gene expression barcode for each tissue type. To do this we took advantage of the vast amount of publicly available datasets. These data were also used to assess the algorithm. With clinical data, we find near perfect predictability of normal from diseased tissue for three cancer studies and one Alzheimer's disease study. The barcode method also discovers new tumor subsets in previously published breast cancer studies that can be used for the prognosis of tumor recurrence and survival time.

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“…Complex statistical algorithms are increasingly used for data modeling and expression change identification. Additionally, comparative approaches have been proposed to evaluate the performance of various algorithms on gene expression data (Bolstad et al, 2003;Cope et al, 2004;Irizarry et al, 2006).…”

Section: Standardizationmentioning

confidence: 99%