A robust measure of correlation between two genes on a microarray.

Citation data:

BMC bioinformatics, ISSN: 1471-2105, Vol: 8, Issue: 1, Page: 220

Publication Year:
2007
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Citations 36
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Repository URL:
https://scholarship.claremont.edu/pomona_fac_pub/287
PMID:
17592643
DOI:
10.1186/1471-2105-8-220
PMCID:
PMC1929126
Author(s):
Hardin, Johanna S.; Mitani, Aya, '06; Hicks, Leanne; VanKoten, Brian
Publisher(s):
Springer Nature
Tags:
Biochemistry, Genetics and Molecular Biology; Computer Science; Mathematics; microarrays; gene expression patterns; Pearson correlation; Applied Statistics; Bioinformatics; Microarrays; Physical Sciences and Mathematics; Statistics and Probability
article description
The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.)