bimod {genefu} | R Documentation

## Function to identify bimodality for gene expression or signature score

### Description

This function fits a mixture of two Gaussians to identify bimodality. Useful to identify ER of HER2 status of breast tumors using ESR1 and ERBB2 expressions respectively.

### Usage

    bimod(x, data, annot, do.mapping = FALSE, mapping, model = c("E", "V"),
    do.scale = TRUE, verbose = FALSE, ...)

### Arguments

`x` | Matrix containing the gene(s) in the gene list in rows and at least three columns: "probe", "EntrezGene.ID" and "coefficient" standing for the name of the probe, the NCBI Entrez Gene id and the coefficient giving the direction and the strength of the association of each gene in the gene list.
`data` | Matrix of gene expressions with samples in rows and probes in columns, dimnames being properly defined.
`annot` | Matrix of annotations with at least one column named "EntrezGene.ID", dimnames being properly defined.
`do.mapping` | TRUE if the mapping through Entrez Gene ids must be performed (in case of ambiguities, the most variant probe is kept for each gene), FALSE otherwise.
`mapping` | Matrix with columns "EntrezGene.ID" and "probe" used to force the mapping such that the probes are not selected based on their variance.
`model` | Model name used in Mclust.
`do.scale` | TRUE if the gene expressions or signature scores must be rescaled (see rescale), FALSE otherwise.
`verbose` | TRUE to print informative messages, FALSE otherwise.
`...` | Additional parameters to pass to sig.score.

### Value

A list with items:

- status: Status being 0 or 1.

- status1.proba: Probability p to be of status 1, the probability to be of status 0 being 1-p.

- gaussians: Matrix of parameters fitted in the mixture of two Gaussians. Matrix of NA values if EM algorithm did not converge.

- BIC: Values (gene expressions or signature scores) used to identify bimodality.

- BI: Bimodality Index (BI) as defined by Wang et al., 2009.

- x: Values (gene expressions or signature scores) used to identify bimodality

### References

Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, Delorenzi M, Piccart M, and Sotiriou C (2008) "Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes", Clinical Cancer Research, 14(16):5158–5165. Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, Desmedt C, Ignatiadis M, Sengstag T, Schutz F, Goldstein DR, Piccart MJ and Delorenzi M (2008) "Meta-analysis of Gene-Expression Profiles in Breast Cancer: Toward a Unified Understanding of Breast Cancer Sub-typing and Prognosis Signatures", Breast Cancer Research, 10(4):R65. Fraley C and Raftery E (2002) "Model-Based Clustering, Discriminant Analysis, and Density Estimation", Journal of American Statistical Asscoiation, 97(458):611–631. Wang J, Wen S, Symmans FW, Pusztai L and Coombes KR (2009) "The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data", Cancer Informatics, 7:199–216.

### See Also

mclust::Mclust

### Examples

    # load NKI data
    data(nkis)
    # load gene modules from Desmedt et al. 2008
    data(mod1)
    # retrieve esr1 affy probe and Entrez Gene id
    esr1 <- mod1$ESR1[1, ,drop=FALSE]
    # computation of signature scores
    esr1.bimod <- bimod(x=esr1, data=data.nkis, annot=annot.nkis, do.mapping=TRUE,
      model="V", verbose=TRUE)
    table("ER.IHC"=demo.nkis[ ,"er"], "ER.GE"=esr1.bimod$status)

[Package genefu version 2.26.0 Index]