eigengenes33 {Pigengene} | R Documentation

## Eigengenes of 33 modules

### Description

This list contains partial eigengenes computed from AML and MDS gene expression profiles provided by Mills et al. These data are included to illustrate how to use `Pigengene-package` and also to facilitate reproducing the results presented in the corresponding paper.

### Usage

    data(eigengenes33)

### Format

A list

### Details

The top 9166 differentially expressed genes were identified and their expressions in AML were used for identifying 33 modules. The first column, ME0, corresponds to module 0 (outliers) and is usually ignored. The eigengene for each module was obtained using `compute.pigengene` function. Oversampling was performed with `amplification=5` to adjust for unbalanced sample-size.

### Value

It is a list of 3 objects:

`aml` A 202 by 34 matrix. Each column reports the values of a module eigengene for AML cases.

`mds` A 164 by 34 matrix for MDS cases with columns similar to aml.

`modules` A numeric vector of length 9166 labeling members of each module. Named by Entrez ID.

### Source

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15061

### References

Mills, Ken I., et al. (2009). Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. Blood 114.5: 1063-1072.

### See Also

`Pigengene-package`, `compute.pigengene`, `aml`, `mds`, `learn.bn`

### Examples

    library(pheatmap)
    data(eigengenes33)
    pheatmap(eigengenes33$aml,show_rownames=FALSE)
    ## See Pigengene::learn.bn() documentation for more examples.

[Package Pigengene version 1.4.20 Index]