Molecular markers
Microarray
Common errors

Author: Rodney E. Shackelford, D.O., Ph.D. (see Authors page)

Revised: 1 June 2018, last major update July 2010

Copyright: (c) 2008-2018, PathologyOutlines.com, Inc.

PubMed Search: Microarray common errors

Cite this page: Shackelford, R.E. Microarray - common errors. PathologyOutlines.com website. http://www.pathologyoutlines.com/topic/molecularpathmicroarrayerrors.html. Accessed July 18th, 2018.
Definition / general
  • Like any other technique, microarray is subject to several common errors
Assay complexity
  • Cloning and PCR steps required to create and process up to one million different sequences, combined with printing these sequences on the microarray chip, is extremely complex
  • Any error in this process will result in the misidentification of an expressed sequence, giving false data
Signal variation and analysis
  • Hybridization step, washing and pixel quantification steps are complicated by many factors, including background fluorescence, uneven hybridization, fluorophore inactivation by ozone and light exposure, temperature variation, cover slip positioning, hybridization time, uneven hybridization and dye leaking giving a false signal
  • Many other small but important details can significantly alter microarray results
  • Locating and troubleshooting these problems can be complex, expensive and time consuming
Incomplete oligonucleotide and cDNA synthesis
  • Extreme care must be taken to insure the quality and sequence integrity of the microarray probes because unrecognized incomplete or altered probes will drastically alter the hybridization step, invalidating assay results
  • Sheer number of probes required in many microarray assays can make this insuring the integrity of the probes very difficult
Data analysis and evaluation
  • Each microarray data set can consist of several million data points
  • Additionally, each microarray result is typically repeated multiple times, giving an enormous amount of raw data to be analyzed
  • Microarray experimental validity rests on multiple complex steps, such as defining the areas to be pixilated and counted, setting and subtracting out the appropriate background level, setting the boundaries of expression significance, compiling the results of multiple experiments and choosing the most appropriate method of statistical analysis
  • Any error at one of these steps will compromise the final data
  • Background intensity analysis can be especially complex in high density microarrays, compared to spotted microarrays, as the former lacks spaces between the probes