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Last data update: 2014.03.03

R: Function to identify breast cancer molecular subtypes using...

subtype.cluster.predict

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

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(genefu)
Loading required package: survcomp
Loading required package: survival
Loading required package: prodlim
Loading required package: mclust
Package 'mclust' version 5.2
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: limma
Loading required package: biomaRt
Loading required package: iC10
Loading required package: pamr
Loading required package: cluster
Loading required package: iC10TrainingData
Loading required package: AIMS
Loading required package: e1071
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genefu/subtype.cluster.predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: subtype.cluster.predict
> ### Title: Function to identify breast cancer molecular subtypes using the
> ###   Subtype Clustering Model
> ### Aliases: subtype.cluster.predict
> ### Keywords: clustering
> 
> ### ** 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)

ER+/HER2- ER-/HER2-     HER2+ 
       69        56        25 
> table(sbt.vdxs$subtype2)

ER+/HER2- High Prolif  ER+/HER2- Low Prolif             ER-/HER2- 
                   42                    27                    56 
                HER2+ 
                   25 
> 
> ## 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)

ER+/HER2- ER-/HER2-     HER2+ 
       99        27        24 
> table(sbt.nkis$subtype2)

ER+/HER2- High Prolif  ER+/HER2- Low Prolif             ER-/HER2- 
                   47                    52                    27 
                HER2+ 
                   24 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>