"Harshlighting" small blemishes on microarrays

BMC Bioinformatics. 2005 Mar 22:6:65. doi: 10.1186/1471-2105-6-65.

Abstract

Background: Microscopists are familiar with many blemishes that fluorescence images can have due to dust and debris, glass flaws, uneven distribution of fluids or surface coatings, etc. Microarray scans show similar artefacts, which affect the analysis, particularly when one tries to detect subtle changes. However, most blemishes are hard to find by the unaided eye, particularly in high-density oligonucleotide arrays (HDONAs).

Results: We present a method that harnesses the statistical power provided by having several HDONAs available, which are obtained under similar conditions except for the experimental factor. This method "harshlights" blemishes and renders them evident. We find empirically that about 25% of our chips are blemished, and we analyze the impact of masking them on screening for differentially expressed genes.

Conclusion: Experiments attempting to assess subtle expression changes should be carefully screened for blemishes on the chips. The proposed method provides investigators with a novel robust approach to improve the sensitivity of microarray analyses. By utilizing topological information to identify and mask blemishes prior to model based analyses, the method prevents artefacts from confounding the process of background correction, normalization, and summarization.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms
  • Artifacts
  • Computational Biology / methods*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Gene Expression Profiling
  • Humans
  • Microarray Analysis
  • Microscopy, Fluorescence / methods
  • Models, Genetic
  • Models, Statistical
  • Multivariate Analysis
  • Nucleic Acid Hybridization
  • Oligonucleotide Array Sequence Analysis / instrumentation*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Principal Component Analysis
  • Psoriasis / genetics
  • Reproducibility of Results
  • Sensitivity and Specificity