p53DataSet {GSAR} | R Documentation

## p53 Dataset of the NCI-60 Cell Lines

### Description

A matrix of gene expression profiles for a processed version of the p53 dataset obtained from the NCI-60 cell lines using the hgu95av2 microarray platform.

### Usage

    data(p53DataSet)

### Format

A matrix of 8655 rows and 50 columns where rows correspond to genes and columns correspond to samples. Gene symbol identifiers are used for rows. Column names indicate the class of the samples (wild type p53 or mutated p53) with the first 17 column names starting with `WT1` and ending with `WT17` and next 33 column names starting with `MUT1` and ending with `MUT33`.

### Details

p53 is a major tumor suppressor protein. The p53 dataset comprises 50 samples of NCI-60 cell lines differentiated based on the status of the TP53 gene: 17 cell lines carrying wild type (WT) TP53 and 33 cell lines carrying mutated (MUT) TP53 (Olivier et. al. 2002, Subramanian et. al. 2005). Transcriptional profiles obtained from microarrays of platform hgu95av2 were obtained from the available datasets at the GSEA Broad Institute's website.

Probe level intensities were quantile normalized and transformed to the log scale using log2(1 + intensity). Probes originally had Affymetrix identifiers which are mapped to unique gene symbol identifiers. Probes without mapping to entrez and gene symbol identifiers were discarded. Probes with duplicate intensities were assessed and the probe with the largest absolute value of t-statistic between WT and MUT conditions was selected as the gene match. Genes were assigned gene symbol identifiers and columns were assigned names indicating weither they belong to WT or MUT condition. The columns were sorted such that the first 17 columns are WT samples and the next 33 columns are the MUT samples. `p53DataSet` was used in the analysis presented in Rahmatallah et. al. 2014.

### Source

Broad Institute (http://www.broadinstitute.org/gsea/datasets.jsp)

### References

Rahmatallah Y., Emmert-Streib F. and Glazko G. (2014) Gene sets net correlations analysis (GSNCA): a multivariate differential coexpression test for gene sets. Bioinformatics 30, 360–368.

Subramanian A., Tamayo P., Mootha V., Mukherjee S., Ebert B., Gillette M., Paulovich A., Pomeroy S., Golub T., Lander E. and Mesirov J. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102, 15545–15550.

Olivier M., Eeles R., Hollstein M., Khan M., Harris C. and Hainaut P. (2002) The IARC TP53 database: new online mutation analysis and recommendations to users. Hum. Mutat. 19, 607–614.

### Examples

    data(p53DataSet)
    dim(p53DataSet)

[Package GSAR version 1.16.0 Index]