computeGES {TNBC.CMS} | R Documentation

## Computation of gene expression signature scores.

### Description

Computes gene expression signature scores. Also draws boxplots representing the average signature scores for each subtype.

### Usage

    computeGES(expr, pred, rnaseq = FALSE)

### Arguments

`expr` | A `SummarizedExperiment` object or a matrix containig gene expression profiles. If input is a `SummarizedExperiment`, the first element in the assays list should be a matrix of gene expression. Rows and columns of the gene expression matrix correspond to genes and samples, respectively (rownames must be to gene symbols).
`pred` | A vector of predicted consensus molecular subtypes.
`rnaseq` | logical to determine if input data is RNA-Seq gene expression profile. By default, it is FALSE.

### Details

`computeGES` calculates the following 7 gene expression signature scores:

- EMT (epithelial-mesenchymal transition): average of expression values of genes included in the EMT signature published by Tan et al. (2014).

- Stromal: stromal score representing the presence of stromal cells in tumor tissues (computed using the ESTIMATE algorithm).

- Immune: immune score representing the presence of immune cells in tumor tissues (computed using the ESTIMATE algorithm).

- Microenvironment: microenvironment score representing the sum of all immune and stromal cell types (computed using xCell)

- Stemness: stemness index computed using the method developed by Malta et al. (2018).

- Hormone: average of expression values of AR, ERBB2, ESR1, and PGR.

- CIN (chromosomal instability): average of expression values of genes included in the CIN70 signature published by Carter et al. (2006).

### Value

A matrix of gene expression signature scores.

### References

Aran, D. et al. (2017). xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome biology, 18, 220.

Carter, S.L. et al. (2006). A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nature genetics, 38, 1043.

Malta, T.M. et al. (2018). Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell, 173, 338-354.

Tan, T.Z. et al. (2014). Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients. EMBO molecular medicine, 6, 1279-93.

Yoshihara, K. et al. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature communications, 4, 2612.

### Examples

    # Load gene expression profiles of TNBC samples
    data(GSE25055)

    # Predict consensus molecular subtypes of TNBC samples
    prediction <- predictCMS(expr = GSE25055)

    # Compute gene expression signature scores
    resultGES <- computeGES(expr = GSE25055, pred = prediction, rnaseq = FALSE)

[Package TNBC.CMS version 1.10.0 Index]