CORREP (version 1.38.0)

gal_all: Galactose Metabolism Data

Description

The data is compiled by Mario Medvedovic et al, 2003 based on the original full data reported in Ideker et al, 2001. There are a total of 205 rows (genes), 20 experiments, and 4 repeated measurements in the data. There are 4 classes (which correspond to functional categories). The data contains approximately 8 of missing data. The missing values were filled by applying k-nearest neighbor (k = 12) to impute all the missing values.

Usage

data(gal_all)

Format

A data frame with 205 variables on the following 80 replicated observations.

wtRG1

a numeric vector

wtRG2

a numeric vector

wtRG3

a numeric vector

wtRG4

a numeric vector

gal1RG1

a numeric vector

gal1RG2

a numeric vector

gal1RG3

a numeric vector

gal1RG4

a numeric vector

gal2RG1

a numeric vector

gal2RG2

a numeric vector

gal2RG3

a numeric vector

gal2RG4

a numeric vector

gal3RG1

a numeric vector

gal3RG2

a numeric vector

gal3RG3

a numeric vector

gal3RG4

a numeric vector

gal4RG1

a numeric vector

gal4RG2

a numeric vector

gal4RG3

a numeric vector

gal4RG4

a numeric vector

gal5RG1

a numeric vector

gal5RG2

a numeric vector

gal5RG3

a numeric vector

gal5RG4

a numeric vector

gal6RG1

a numeric vector

gal6RG2

a numeric vector

gal6RG3

a numeric vector

gal6RG4

a numeric vector

gal7RG1

a numeric vector

gal7RG2

a numeric vector

gal7RG3

a numeric vector

gal7RG4

a numeric vector

gal10RG1

a numeric vector

gal10RG2

a numeric vector

gal10RG3

a numeric vector

gal10RG4

a numeric vector

gal80RG1

a numeric vector

gal80RG2

a numeric vector

gal80RG3

a numeric vector

gal80RG4

a numeric vector

wtR1

a numeric vector

wtR2

a numeric vector

wtR3

a numeric vector

wtR4

a numeric vector

gal1R1

a numeric vector

gal1R2

a numeric vector

gal1R3

a numeric vector

gal1R4

a numeric vector

gal2R1

a numeric vector

gal2R2

a numeric vector

gal2R3

a numeric vector

gal2R4

a numeric vector

gal3R1

a numeric vector

gal3R2

a numeric vector

gal3R3

a numeric vector

gal3R4

a numeric vector

gal4R1

a numeric vector

gal4R2

a numeric vector

gal4R3

a numeric vector

gal4R4

a numeric vector

gal5R1

a numeric vector

gal5R2

a numeric vector

gal5R3

a numeric vector

gal5R4

a numeric vector

gal6R1

a numeric vector

gal6R2

a numeric vector

gal6R3

a numeric vector

gal6R4

a numeric vector

gal7R1

a numeric vector

gal7R2

a numeric vector

gal7R3

a numeric vector

gal7R4

a numeric vector

gal10R1

a numeric vector

gal10R2

a numeric vector

gal10R3

a numeric vector

gal10R4

a numeric vector

gal80R1

a numeric vector

gal80R2

a numeric vector

gal80R3

a numeric vector

gal80R4

a numeric vector

Source

http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic\_bioinf2003.html

Details

The 205 genes have been classified into four functional classes based on their GO annotations. In the data examaple provided in the vignette, we assume the four classes as true memberships (external knowledge) and use it to evaluate the performances of different correlation measured based clustering methods.

References

Medvedovic M, Yeung KY and Bumgarner RE. 2004. Bayesian Mixture Model Based Clustering of Replicated Microarray Data. Bioinformatics, 22;20(8):1222-32. Ideker, T., Thorsson, V., Siegel, A. and Hood, L. Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of DNA Microarray Data. Journal of Computational Biology 7: 805-817 (2000).

Examples

data(gal_all)
## maybe str(gal_all) ; plot(gal_all) ...