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

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Description

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

Usage

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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

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## 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)

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