subtype.cluster.predict: Function to identify breast cancer molecular subtypes using...

In bhklab/genefu: Computation of Gene Expression-Based Signatures in Breast Cancer

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

A list with items:

• 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).

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)
data(scmgene.robust)

# 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)

bhklab/genefu documentation built on June 2, 2022, 2:56 p.m.