Cited by 862Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsGenomics and Phylogenetic Studies

Multiple-laboratory comparison of microarray platforms

Irizarry, Rafael A, Warren, Daniel, Spencer, Forrest, Kim, Irene F, Biswal, Shyam, Frank, Bryan C, Gabrielson, Edward, Garcia, Joe G N +17 more·2005·Nature Methods

Abstract

Microarray technology is a powerful tool for measuring RNA expression for thousands of genes at once. Various studies have been published comparing competing platforms with mixed results: some find agreement, others do not. As the number of researchers starting to use microarrays and the number of cross-platform meta-analysis studies rapidly increases, appropriate platform assessments become more important. Here we present results from a comparison study that offers important improvements over those previously described in the literature. In particular, we noticed that none of the previously published papers consider differences between labs. For this study, a consortium of ten laboratories from the Washington, DC-Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples. We used appropriate statistical analysis to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.

Integrity signals

Registry

Crossref · 10.1038/nmeth756

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Previous benchmarks of DE analysis methods for single-cell transcriptomics have relied heavily on simulated data or compared methods without ground truth.

    “Previous benchmarks of DE analysis methods for single-cell transcriptomics have relied heavily on simulated data, or else have compared the results of different methods in scenarios where no ground truth was available10,17.”

The citation lists haven’t been fetched yet

References 15 · Cited by 862

Authors
Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., Gabrielson, E., Garcia, J. G. N., Geoghegan, J., Germino, G., Griffin, C., Hilmer, S. C., Hoffman, E., Jedlicka, A. E., Kawasaki, E., Martínez-Murillo, F., Morsberger, L., Lee, H., Petersen, D., Quackenbush, J., Scott, A., Wilson, M., Yang, Y., Ye, S. Q., & Yu, W.
Affiliations
  • Johns Hopkins University (US)
  • J. Craig Venter Institute (US)
  • National Institutes of Health (US)
  • Children's National (US)
  • National Institute of Allergy and Infectious Diseases (US)
Corresponding author
Irizarry, Rafael A
Venue
Nature Methods
Published
2005-04-21
Volume
2
Issue
5
Pages
345-350
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W2141009231
Cited by
862
References
15
Citations by year
2026: 8 · 2025: 6 · 2024: 6 · 2023: 13 · 2022: 12 · 2021: 17 · 2020: 19 · 2019: 13 · 2018: 14 · 2017: 19 · 2016: 17 · 2015: 27 · 2014: 31 · 2013: 43 · 2012: 46
FWCI
46.60
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Methods (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (5u24dk058757-03)
  • National Institute of Diabetes and Digestive and Kidney Diseases (U24DK58757)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
microarray platform comparison, inter-laboratory variability, RNA expression profiling
MeSH terms
District of Columbia, Humans, Laboratories, Baltimore, Reproducibility of Results, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review