subtype.cluster.predict {genefu} | R Documentation

## Function to identify breast cancer molecular subtypes using the Subtype Clustering Model

### Description

This function identifies the breast cancer molecular subtypes using a Subtype Clustering Model fitted by `subtype.cluster`.

### Usage

    subtype.cluster.predict(sbt.model, data, annot, do.mapping = FALSE,
      mapping, do.prediction.strength = FALSE,
      do.BIC = FALSE, plot = FALSE, verbose = FALSE)

### Arguments

`sbt.model` | Subtype Clustering Model as returned by `subtype.cluster`.
`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` | **DEPRECATED** Matrix with columns "EntrezGene.ID" and "probe" used to force the mapping such that the probes are not selected based on their variance.
`do.prediction.strength` | `TRUE` if the prediction strength must be computed (Tibshirani and Walther 2005), `FALSE` otherwise.
`do.BIC` | `TRUE` if the Bayesian Information Criterion must be computed for number of clusters ranging from 1 to 10, `FALSE` otherwise.
`plot` | `TRUE` if the patients and their corresponding subtypes must be plotted, `FALSE` otherwise.
`verbose` | `TRUE` to print informative messages, `FALSE` otherwise.

### Value

`subtype` | Subtypes identified by the Subtype Clustering Model. Subtypes can be either "ER-/HER2-", "HER2+" or "ER+/HER2-".
`subtype.proba` | Probabilities to belong to each subtype estimated by the Subtype Clustering Model.
`prediction.strength` | Prediction strength for `subtypes`.
`BIC` | Bayesian Information Criterion for the Subtype Clustering Model with number of clusters ranging from 1 to 10.
`subtype2` | Subtypes identified by the Subtype Clustering Model using AURKA to discriminate low and high proliferative tumors. Subtypes can be either "ER-/HER2-", "HER2+", "ER+/HER2- High Prolif" or "ER+/HER2- Low Prolif".
`subtype.proba2` | Probabilities to belong to each subtype (including discrimination between lowly and highly proliferative ER+/HER2- tumors, see `subtype2`) estimated by the Subtype Clustering Model.
`prediction.strength2` | Prediction strength for `subtypes2`.
`module.scores` | Matrix containing ESR1, ERBB2 and AURKA module scores.
`mapping` | Mapping if necessary (list of matrices with 3 columns: probe, EntrezGene.ID and new.probe).

### Author(s)

Benjamin Haibe-Kains

### 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.

Tibshirani R and Walther G (2005) "Cluster Validation by Prediction Strength", Journal of Computational and Graphical Statistics, 14(3):511–528

### See Also

`subtype.cluster`, `scmod1.robust`, `scmod2.robust`

### Examples

    ## without mapping (affy hgu133a or plus2 only)
    ## load VDX data
    data(vdxs)
    ## Subtype Clustering Model fitted on EXPO and applied on VDX
    sbt.vdxs <- subtype.cluster.predict(sbt.model=scmgene.robust, data=data.vdxs,
      annot=annot.vdxs, do.mapping=FALSE, do.prediction.strength=FALSE,
      do.BIC=FALSE, plot=TRUE, verbose=TRUE)
    table(sbt.vdxs$subtype)
    table(sbt.vdxs$subtype2)

    ## with mapping
    ## load NKI data
    data(nkis)
    ## Subtype Clustering Model fitted on EXPO and applied on NKI
    sbt.nkis <- subtype.cluster.predict(sbt.model=scmgene.robust, data=data.nkis,
      annot=annot.nkis, do.mapping=TRUE, do.prediction.strength=FALSE,
      do.BIC=FALSE, plot=TRUE, verbose=TRUE)
    table(sbt.nkis$subtype)
    table(sbt.nkis$subtype2)

[Package genefu version 2.20.0 Index]