Quality control is an important issue for microarrays. The programs here give some images
and other information to assess the extent of regional biases and some other artifacts on your microarrays.
Regional bias refers to when one region of a chip shows artifactually high or low intensities (or ratios in
a two-channel array) relative to the majority of the chip.
Quality-control is an important issues in the analysis of gene expression microarrays. One type of problem
is regional bias, in which one region of a chip shows artifactually high or low intensities (or ratios in a
two-channel array) relative to the majority of the chip. Current practice in quality assessment for microarrays
does not address regional biases.
We present methods implemented in R for visualizing regional biases and other spatial artifacts on spotted
microarrays and Affymetrix chips. We also propose a statistical index to quantify regional bias and investigate
its typical distribution on spotted and Affymetrix arrays.
We demonstrate that notable regional biases occur on both Affymetrix and spotted arrays and that they can make
a significant difference in the case of spotted microarray results. Although strong biases are also seen at the
level of individual probes on Affymetrix chips, the gene expression measures are less affected, especially when
the RMA method is used to summarize intensities for the probe sets. A web application program for visualization
and quantitation of regional bias is provided at http://www.discover.nci.nih.gov/affytools.
Researchers should visualize and measure the regional biases and should estimate their impact on gene expression measurements obtained. Here, we (i) introduce pictorial visualizations of the spatial biases; (ii) present for Affymetrix chips a useful resolution of the biases into two components, one related to background, the other to intensity scale factor; (iii) introduce a single parameter to reflect the global bias present across an array. We also examine the pattern distribution of such biases and conclude that algorithms based on smoothing are unlikely to compensate adequately for them.
SmudgeMiner is a development of the Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology (LMP), Center for Cancer Research (CCR) National Cancer Institute (NCI). If you have any problems, questions or feedback on the tool, please email us.