subtype.cluster.predict {genefu} | R Documentation |
This function identifies the breast cancer molecular subtypes using a Subtype Clustering Model fitted by subtype.cluster
.
subtype.cluster.predict(sbt.model, data, annot, do.mapping = FALSE, mapping, do.prediction.strength = FALSE, do.BIC = FALSE, plot = FALSE, verbose = FALSE)
sbt.model |
Subtype Clustering Model as returned by |
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 |
|
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 |
|
do.BIC |
|
plot |
|
verbose |
|
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 |
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 |
prediction.strength2 |
Prediction strength for |
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). |
Benjamin Haibe-Kains
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
subtype.cluster
, scmod1.robust
, scmod2.robust
## 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)