Adjustment of systematic microarray data biases
Abstract
Motivation: Systematic differences due to experimental features of microarray experiments are present in most large microarray data sets. Many different experimental features can cause biases including different sources of RNA, different production lots of microarrays or different microarray platforms. These systematic effects present a substantial hurdle to the analysis of microarray data. Results: We present here a new method for the identification and adjustment of systematic biases that are present within microarray data sets. Our approach is based on modern statistical discrimination methods and is shown to be very effective in removing systematic biases present in a previously published breast tumor cDNA microarray data set. The new method of ‘Distance Weighted Discrimination (DWD)’ is shown to be better than Support Vector Machines and Singular Value Decomposition for the adjustment of systematic microarray effects. In addition, it is shown to be of general use as a tool for the discrimination of systematic problems present in microarray data sets, including the merging of two breast tumor data sets completed on different microarray platforms. Availability: Matlab software to perform DWD can be retrieved from https://genome.unc.edu/pubsup/dwd/ Supplementary information: The complete figures that represent the cluster diagrams in Figure 6 and other figures are available at https://genome.unc.edu/pubsup/dwd/
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- Authors
- Benito, M., Parker, J., Du, Q., Wu, J., Xiang, D., Perou, C. M., & Marron, J. S.
- Affiliations
- UNC Lineberger Comprehensive Cancer Center (US)
- Karolinska Institutet (SE)
- University of North Carolina at Chapel Hill (US)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-01-01
- Volume
- 20
- Issue
- 1
- Pages
- 105-114
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2139345901
- Cited by
- 426
- References
- 14
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 9 · 2023: 22 · 2022: 13 · 2021: 13 · 2020: 25 · 2019: 21 · 2018: 28 · 2017: 18 · 2016: 24 · 2015: 24 · 2014: 27 · 2013: 34 · 2012: 34
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- systematic bias, distance weighted discrimination, data merging, statistical discrimination, support vector machine, singular value decomposition
- MeSH terms
- Algorithms, Models, Genetic, Quality Control, Sensitivity and Specificity, Genetic Variation, Reproducibility of Results, Models, Statistical, Selection Bias, Cluster Analysis, Discriminant Analysis, Sequence Alignment, Sequence Analysis, DNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Principal Component Analysis
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