Dec 18, 2010

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies

BioData Mining
Derrick K Rollins, Ailing Teh

Abstract

Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (P...Continue Reading

  • References18
  • Citations2

References

  • References18
  • Citations2

Citations

Mentioned in this Paper

Ethanol
DNA Microarray Format
MSTN gene
Ethanol Measurement
Genome
Pseudo brand of pseudoephedrine
PTH gene
Gene Expression
GDF8 protein, human
Gene Signature

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