Are data from different gene expression microarray platforms comparable?

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2...

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Veröffentlicht in:Genomics (San Diego, Calif.) Calif.), 2004-06, Vol.83 (6), p.1164-1168
Hauptverfasser: JÄRVINEN, Anna-Kaarina, HAUTANIEMI, Sampsa, EDGREN, Henrik, AUVINEN, Petri, SAARELA, Janna, KALLIONIEMI, Olli-P, MONNI, Outi
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container_end_page 1168
container_issue 6
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container_title Genomics (San Diego, Calif.)
container_volume 83
creator JÄRVINEN, Anna-Kaarina
HAUTANIEMI, Sampsa
EDGREN, Henrik
AUVINEN, Petri
SAARELA, Janna
KALLIONIEMI, Olli-P
MONNI, Outi
description Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.
doi_str_mv 10.1016/j.ygeno.2004.01.004
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source MEDLINE; Elsevier ScienceDirect Journals
subjects Biological and medical sciences
Cell Line, Tumor
Data Interpretation, Statistical
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - instrumentation
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic - genetics
Gene Library
Genes. Genome
Genetics of eukaryotes. Biological and molecular evolution
Gynecology. Andrology. Obstetrics
Humans
Mammary gland diseases
Medical sciences
Molecular and cellular biology
Molecular genetics
Oligonucleotide Array Sequence Analysis - instrumentation
Oligonucleotide Array Sequence Analysis - methods
Quality Control
Tumors
title Are data from different gene expression microarray platforms comparable?
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