gal_all: Galactose Metabolism Data
In CORREP: Multivariate Correlation Estimator and Statistical Inference Procedures.
Description
Usage
Format
Details
Source
References
Examples
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
1
Format
A data frame with 205 variables on the following 80 replicated observations.
wtRG1a numeric vector
wtRG2a numeric vector
wtRG3a numeric vector
wtRG4a numeric vector
gal1RG1a numeric vector
gal1RG2a numeric vector
gal1RG3a numeric vector
gal1RG4a numeric vector
gal2RG1a numeric vector
gal2RG2a numeric vector
gal2RG3a numeric vector
gal2RG4a numeric vector
gal3RG1a numeric vector
gal3RG2a numeric vector
gal3RG3a numeric vector
gal3RG4a numeric vector
gal4RG1a numeric vector
gal4RG2a numeric vector
gal4RG3a numeric vector
gal4RG4a numeric vector
gal5RG1a numeric vector
gal5RG2a numeric vector
gal5RG3a numeric vector
gal5RG4a numeric vector
gal6RG1a numeric vector
gal6RG2a numeric vector
gal6RG3a numeric vector
gal6RG4a numeric vector
gal7RG1a numeric vector
gal7RG2a numeric vector
gal7RG3a numeric vector
gal7RG4a numeric vector
gal10RG1a numeric vector
gal10RG2a numeric vector
gal10RG3a numeric vector
gal10RG4a numeric vector
gal80RG1a numeric vector
gal80RG2a numeric vector
gal80RG3a numeric vector
gal80RG4a numeric vector
wtR1a numeric vector
wtR2a numeric vector
wtR3a numeric vector
wtR4a numeric vector
gal1R1a numeric vector
gal1R2a numeric vector
gal1R3a numeric vector
gal1R4a numeric vector
gal2R1a numeric vector
gal2R2a numeric vector
gal2R3a numeric vector
gal2R4a numeric vector
gal3R1a numeric vector
gal3R2a numeric vector
gal3R3a numeric vector
gal3R4a numeric vector
gal4R1a numeric vector
gal4R2a numeric vector
gal4R3a numeric vector
gal4R4a numeric vector
gal5R1a numeric vector
gal5R2a numeric vector
gal5R3a numeric vector
gal5R4a numeric vector
gal6R1a numeric vector
gal6R2a numeric vector
gal6R3a numeric vector
gal6R4a numeric vector
gal7R1a numeric vector
gal7R2a numeric vector
gal7R3a numeric vector
gal7R4a numeric vector
gal10R1a numeric vector
gal10R2a numeric vector
gal10R3a numeric vector
gal10R4a numeric vector
gal80R1a numeric vector
gal80R2a numeric vector
gal80R3a numeric vector
gal80R4a numeric vector
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.
Source
http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic\_bioinf2003.html
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
1
2
Example output
CORREP documentation built on Nov. 8, 2020, 5:09 p.m.
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Description Usage Format Details Source References Examples
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
1 |
Format
A data frame with 205 variables on the following 80 replicated observations.
wtRG1a numeric vector
wtRG2a numeric vector
wtRG3a numeric vector
wtRG4a numeric vector
gal1RG1a numeric vector
gal1RG2a numeric vector
gal1RG3a numeric vector
gal1RG4a numeric vector
gal2RG1a numeric vector
gal2RG2a numeric vector
gal2RG3a numeric vector
gal2RG4a numeric vector
gal3RG1a numeric vector
gal3RG2a numeric vector
gal3RG3a numeric vector
gal3RG4a numeric vector
gal4RG1a numeric vector
gal4RG2a numeric vector
gal4RG3a numeric vector
gal4RG4a numeric vector
gal5RG1a numeric vector
gal5RG2a numeric vector
gal5RG3a numeric vector
gal5RG4a numeric vector
gal6RG1a numeric vector
gal6RG2a numeric vector
gal6RG3a numeric vector
gal6RG4a numeric vector
gal7RG1a numeric vector
gal7RG2a numeric vector
gal7RG3a numeric vector
gal7RG4a numeric vector
gal10RG1a numeric vector
gal10RG2a numeric vector
gal10RG3a numeric vector
gal10RG4a numeric vector
gal80RG1a numeric vector
gal80RG2a numeric vector
gal80RG3a numeric vector
gal80RG4a numeric vector
wtR1a numeric vector
wtR2a numeric vector
wtR3a numeric vector
wtR4a numeric vector
gal1R1a numeric vector
gal1R2a numeric vector
gal1R3a numeric vector
gal1R4a numeric vector
gal2R1a numeric vector
gal2R2a numeric vector
gal2R3a numeric vector
gal2R4a numeric vector
gal3R1a numeric vector
gal3R2a numeric vector
gal3R3a numeric vector
gal3R4a numeric vector
gal4R1a numeric vector
gal4R2a numeric vector
gal4R3a numeric vector
gal4R4a numeric vector
gal5R1a numeric vector
gal5R2a numeric vector
gal5R3a numeric vector
gal5R4a numeric vector
gal6R1a numeric vector
gal6R2a numeric vector
gal6R3a numeric vector
gal6R4a numeric vector
gal7R1a numeric vector
gal7R2a numeric vector
gal7R3a numeric vector
gal7R4a numeric vector
gal10R1a numeric vector
gal10R2a numeric vector
gal10R3a numeric vector
gal10R4a numeric vector
gal80R1a numeric vector
gal80R2a numeric vector
gal80R3a numeric vector
gal80R4a numeric vector
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.
Source
http://expression.microslu.washington.edu/expression/kayee/medvedovic2003/medvedovic\_bioinf2003.html
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
1 2 |
Example output
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