A benchmark for Affymetrix GeneChip expression measures

Leslie M. Cope; Rafael A. Irizarry; Harris A. Jaffee; Zhijin Wu; Terence P. Speed

doi:10.1093/bioinformatics/btg410

A benchmark for Affymetrix GeneChip expression measures

Leslie M. Cope, Rafael A. Irizarry, Harris A. Jaffee, Zhijin Wu, Terence P. Speed

School of Medicine

Research output: Contribution to journal › Article › peer-review

226 Scopus citations

Abstract

Motivation: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. Results: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.

Original language	English (US)
Pages (from-to)	323-331
Number of pages	9
Journal	Bioinformatics
Volume	20
Issue number	3
DOIs	https://doi.org/10.1093/bioinformatics/btg410
State	Published - Feb 12 2004

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btg410

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title = "A benchmark for Affymetrix GeneChip expression measures",

abstract = "Motivation: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. Results: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.",

author = "Cope, {Leslie M.} and Irizarry, {Rafael A.} and Jaffee, {Harris A.} and Zhijin Wu and Speed, {Terence P.}",

note = "Funding Information: The authors would like to thank Muralidhar Bopparaju, Mohan Parigi, Jiong Yang and Gerald G. Gilyeat for help with the webtool. The work of L.M.C., R.A.I. and H.A.J. is funded by the Hopkins PGA (www.hopkins-genomics.org) Administrative/Bioinformatics Component (P01 HL 66583).",

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AU - Speed, Terence P.

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PY - 2004/2/12

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N2 - Motivation: The defining feature of oligonucleotide expression arrays is the use of several probes to assay each targeted transcript. This is a bonanza for the statistical geneticist, who can create probeset summaries with specific characteristics. There are now several methods available for summarizing probe level data from the popular Affymetrix GeneChips, but it is difficult to identify the best method for a given inquiry. Results: We have developed a graphical tool to evaluate summaries of Affymetrix probe level data. Plots and summary statistics offer a picture of how an expression measure performs in several important areas. This picture facilitates the comparison of competing expression measures and the selection of methods suitable for a specific investigation. The key is a benchmark data set consisting of a dilution study and a spike-in study. Because the truth is known for these data, we can identify statistical features of the data for which the expected outcome is known in advance. Those features highlighted in our suite of graphs are justified by questions of biological interest and motivated by the presence of appropriate data.

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A benchmark for Affymetrix GeneChip expression measures

Abstract

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