breast.TCGA {mixOmics} | R Documentation

## Breast Cancer multi omics data from TCGA

### Description

This data set is a small subset of the full data set from The Cancer Genome Atlas that can be analysed with the DIABLO framework. It contains the expression or abundance of three matching omics data sets: mRNA, miRNA and proteomics for 150 breast cancer samples (Basal, Her2, Luminal A) in the training set, and 70 samples in the test set. The test set is missing the proteomics data set.

### Usage

    data(breast.TCGA)

### Format

A list containing two data sets, `data.train` and `data.test` which both include:

list("miRNA")

  data frame with 150 (70) rows and 184 columns in the training (test) data set. The expression levels of 184 miRNA.

list("mRNA")

  data frame with 150 (70) rows and 520 columns in the training (test) data set. The expression levels of 200 mRNA.

list("protein")

  data frame with 150 (70) rows and 142 columns in the training data set only. The abundance of 142 proteins.

list("subtype")

  a factor indicating the brerast cancer subtypes in the training (length of 150) and test (length of 70) sets.

### Details

The data come from The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/). We divided the data into a training (discovery) and test (validation) set. The protein dataset which had a limited number of subjects available was used to allocate subjects into the training set only, while the tes set included all remaining subject. Each data set was normalised and pre-processed. For illustrative purposes we drastically filtered the data here.

### Value

none

### Source

The raw data were downloaded from http://cancergenome.nih.gov/. The normalised and filtered data we analysed with DIABLO are available on www.mixOmics.org/mixDIABLO

### References

Singh A., Shannon C., Gautier B., Rohart F., Vacher M., Tebbutt S. and Lê Cao K.A. (2019), DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays, Bioinformatics, Volume 35, Issue 17, 1 September 2019, Pages 3055–3062.

[Package mixOmics version 6.14.1 Index]