Package 'genefu'

Description

This function fits a mixture of two Gaussians to identify bimodality. Useful to identify ER of HER2 status of breast tumors using ESR1 and ERBB2 expressions respectively.

Usage

bimod(x, data, annot, do.mapping = FALSE, mapping, model = c("E", "V"),
do.scale = TRUE, verbose = FALSE, ...)

Arguments

Value

A list with items:

• status: Status being 0 or 1.

• status1.proba: Probability p to be of status 1, the probability to be of status 0 being 1-p.

• gaussians: Matrix of parameters fitted in the mixture of two Gaussians. Matrix of NA values if EM algorithm did not converge.

• BIC: Values (gene expressions or signature scores) used to identify bimodality.

• BI: Bimodality Index (BI) as defined by Wang et al., 2009.

• x: Values (gene expressions or signature scores) used to identify bimodality

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. Fraley C and Raftery E (2002) "Model-Based Clustering, Discriminant Analysis, and Density Estimation", Journal of American Statistical Asscoiation, 97(458):611–631. Wang J, Wen S, Symmans FW, Pusztai L and Coombes KR (2009) "The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data", Cancer Informatics, 7:199–216.

See Also

mclust::Mclust

Examples

# load NKI data
data(nkis)
# load gene modules from Desmedt et al. 2008
data(mod1)
# retrieve esr1 affy probe and Entrez Gene id
esr1 <- mod1$ESR1[1, ,drop=FALSE]
# computation of signature scores
esr1.bimod <- bimod(x=esr1, data=data.nkis, annot=annot.nkis, do.mapping=TRUE,
  model="V", verbose=TRUE)
table("ER.IHC"=demo.nkis[ ,"er"], "ER.GE"=esr1.bimod$status)

Description

This function allows for display a boxplot with jittered points.

Usage

boxplotplus2(x, .jit = 0.25, .las = 1, .ylim, box.col = "lightgrey",
 pt.col = "blue", pt.cex = 0.5, pt.pch = 16, med.line = FALSE,
 med.col = "goldenrod", ...)

Arguments

Value

Number of samples in each group.

Note

2.21.2006 - Christos Hatzis, Nuvera Biosciences

See Also

graphics::boxplot, base::jitter

Examples

dd <- list("G1"=runif(20), "G2"=rexp(30) * -1.1, "G3"=rnorm(15) * 1.3)
boxplotplus2(x=dd, .las=3, .jit=0.75, .ylim=c(-3,3), pt.cex=0.75,
  pt.col=c(rep("darkred", 20), rep("darkgreen", 30), rep("darkblue", 15)),
  pt.pch=c(0, 9, 17))

Description

Subtyping method for identifying Claudin-Low Breast Cancer Samples. Code generously provided by Aleix Prat.

Usage

claudinLow(x, classes="", y, nGenes="", priors="equal",
  std=FALSE, distm="euclidean", centroids=FALSE)

Arguments

Value

A list with items:

• predictions

• testData

• distances

• centroids

References

Aleix Prat, Joel S Parker, Olga Karginova, Cheng Fan, Chad Livasy, Jason I Herschkowitz, Xiaping He, and Charles M. Perou (2010) "Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer", Breast Cancer Research, 12(5):R68

See Also

medianCtr(), q

Examples

data(claudinLowData)

#Training Set
train <- claudinLowData
train$xd <-  medianCtr(train$xd)
# Testing Set
test <- claudinLowData
test$xd <-  medianCtr(test$xd)

# Generate Predictions
predout <- claudinLow(x=train$xd, classes=as.matrix(train$classes$Group,ncol=1), y=test$xd)

# Obtain results
results <- cbind(predout$predictions, predout$distances)
#write.table(results,"T.E.9CELL.LINE_results.txt",sep="\t",col=T, row=FALSE)

Description

Training and Testing Data for use with the Claudin-Low Classifier

Usage

data(claudinLowData)

Format

• xd: Matrix of 807 features and 52 samples

• classes: factor to split samples

• nfeatures: number of features

• nsamples: number of samples

• fnames: names of features

• snames: names of samples

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Aleix Prat, Joel S Parker, Olga Karginova, Cheng Fan, Chad Livasy, Jason I Herschkowitz, Xiaping He, and Charles M. Perou (2010) "Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer", Breast Cancer Research, 12(5):R68

See Also

claudinLow()

Description

Utility function called within the claudinLow classifier

Usage

collapseIDs(x,allids=row.names(x),method="mean")

Arguments

Value

A matrix

References

citation("claudinLow")

See Also

claudinLow

Description

This function performs a statistical comparison of the correlation coefficients as computed between each probe and prototype.

Usage

compareProtoCor(gene.cor, proto.cor, nn,
 p.adjust.m = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"))

Arguments

Value

Data frame with probes in rows and with three columns: "proto" is the prototype to which the probe is the most correlated, "cor" is the actual correlation, and "signif" is the (corrected) p-value for the superiority of the correlation to this prototype compared to the second highest correlation.

See Also

compute.proto.cor.meta, compute.pairw.cor.meta

Examples

# load VDX dataset
data(vdxs)
# load NKI dataset
data(nkis)
# reduce datasets
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
# mapping of datasets
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
# define some prototypes
protos <- paste("geneid", ginter[1:3], sep=".")
# compute meta-estimate of correlation coefficients to the three prototype genes
probecor <- compute.proto.cor.meta(datas=datas.mapped$datas, proto=protos,
  method="pearson")
# compute meta-estimate of pairwise correlation coefficients between prototypes
datas.proto <- lapply(X=datas.mapped$datas, FUN=function(x, p) {
  return(x[ ,p,drop=FALSE]) }, p=protos)
protocor <- compute.pairw.cor.meta(datas=datas.proto, method="pearson")
# compare correlation coefficients to each prototype
res <- compareProtoCor(gene.cor=probecor$cor, proto.cor=protocor$cor,
nn=probecor$cor.n, p.adjust.m="fdr")
head(res)

Description

This function computes meta-estimate of pairwise correlation coefficients for a set of genes from a list of gene expression datasets.

Usage

compute.pairw.cor.meta(datas, method = c("pearson", "spearman"))

Arguments

Value

A list with items:

• cor Matrix of meta-estimate of correlation coefficients with probes in rows and prototypes in columns

• cor.n Number of samples used to compute meta-estimate of correlation coefficients.

See Also

map.datasets, compute.proto.cor.meta

Examples

# load VDX dataset
data(vdxs)
# load NKI dataset
data(nkis)
# reduce datasets
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
# mapping of datasets
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
# compute meta-estimate of pairwise correlation coefficients
pairwcor <- compute.pairw.cor.meta(datas=datas.mapped$datas, method="pearson")
str(pairwcor)

Description

This function computes the Z transformation of the meta-estimate of pairwise correlation coefficients for a set of genes from a list of gene expression datasets.

Usage

compute.pairw.cor.z(datas, method = c("pearson"))

Arguments

Value

A list with items: -z Z transformation of the meta-estimate of correlation coefficients. -se Standard error of the Z transformation of the meta-estimate of correlation coefficients. -nn Number of samples used to compute the meta-estimate of correlation coefficients.

See Also

map.datasets, compute.pairw.cor.meta, compute.proto.cor.meta

Description

This function computes meta-estimate of correlation coefficients between a set of genes and a set of prototypes from a list of gene expression datasets.

Usage

compute.proto.cor.meta(datas, proto, method = c("pearson", "spearman"))

Arguments

Value

A list with items: -cor Matrix of meta-estimate of correlation coefficients with probes in rows and prototypes in columns. -cor.n Number of samples used to compute meta-estimate of correlation coefficients.

See Also

map.datasets

Examples

# load VDX dataset
data(vdxs)
# load NKI dataset
data(nkis)
# reduce datasets
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
# mapping of datasets
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
# define some prototypes
protos <- paste("geneid", ginter[1:3], sep=".")
# compute meta-estimate of correlation coefficients to the three prototype genes
probecor <- compute.proto.cor.meta(datas=datas.mapped$datas, proto=protos,
  method="pearson")
str(probecor)

Description

This function tests for statistical differences between two dependent correlations using the formula provided on page 56 of Cohen & Cohen (1983). The function returns a t-value, the DF and the p-value.

Usage

cordiff.dep(r.x1y, r.x2y, r.x1x2, n,
  alternative = c("two.sided", "less", "greater"))

Arguments

Details

This function is inspired from the cordif.dep.

Value

Vector of three values: t statistics, degree of freedom, and p-value.

References

Cohen, J. & Cohen, P. (1983) "Applied multiple regression/correlation analysis for the behavioral sciences (2nd Ed.)" Hillsdale, nJ: Lawrence Erlbaum Associates.

See Also

stats::cor, stats::t.test, compareProtoCor

Examples

# load VDX dataset
data(vdxs)
# retrieve ESR1, AURKA and MKI67 gene expressions
x1 <- data.vdxs[ ,"208079_s_at"]
x2 <- data.vdxs[ ,"205225_at"]
y <- data.vdxs[ ,"212022_s_at"]
# is MKI67 significantly more correlated to AURKA than ESR1?
cc.ix <- complete.cases(x1, x2, y)
cordiff.dep(r.x1y=abs(cor(x=x1[cc.ix], y=y[cc.ix], use="everything",
  method="pearson")), r.x2y=abs(cor(x=x2[cc.ix], y=y[cc.ix],
  use="everything", method="pearson")), r.x1x2=abs(cor(x=x1[cc.ix],
  y=x2[cc.ix], use="everything", method="pearson")), n=sum(cc.ix),
  alternative="greater")

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the endoPredict signature as published by Filipits et al 2011.

Usage

endoPredict(data, annot, do.mapping = FALSE, mapping, verbose = FALSE)

Arguments

Details

The function works best if data have been noralized with MAS5. Note that for Affymetrix HGU datasets, the mapping is not necessary.

Value

A list with items: -score Continuous signature scores -risk Binary risk classification, 1 being high risk and 0 being low risk. -mapping Mapping used if necessary. -probe If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Filipits, M., Rudas, M., Jakesz, R., Dubsky, P., Fitzal, F., Singer, C. F., et al. (2011). "A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors." Clinical Cancer Research, 17(18):6012–6020.

Examples

# load GENE70 signature
data(sig.endoPredict)
# load NKI dataset
data(vdxs)
# compute relapse score
rs.vdxs <- endoPredict(data=data.vdxs, annot=annot.vdxs, do.mapping=FALSE)

Description

This dataset contains (part of) the gene expression, annotations and clinical data from the expO dataset collected by the International Genomics Consortium ().

Usage

data(expos)

Format

expos is a dataset containing three matrices

• data.expos: Matrix containing gene expressions as measured by Affymetrix hgu133plus2 technology (single-channel, oligonucleotides)

• annot.expos: Matrix containing annotations of ffymetrix hgu133plus2 microarray platform

• demo.expos: Clinical information of the breast cancer patients whose tumors were hybridized

Source

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2109

References

International Genomics Consortium, http://www.intgen.org/research-services/biobanking-experience/expo/ McCall MN, Bolstad BM, Irizarry RA. (2010) "Frozen robust multiarray analysis (fRMA)", Biostatistics, 11(2):242-253.

Description

This function allows for computing the weighted mean and weighted variance of a vector of continuous values.

Usage

fuzzy.ttest(x, w1, w2, alternative=c("two.sided", "less", "greater"),
  check.w = TRUE, na.rm = FALSE)

Arguments

Details

The weights w1 and w2 should represent the likelihood for each observation stored in x to belong to the first and second class, respectively. Therefore the values contained in w1 and w2 should lay in [0,1] and ⁠0 <= (w1[i] + w2[i]) <= 1⁠ for i in 0,1,...,n where n is the length of x. The Welch's version of the t test is implemented in this function, therefore assuming unequal sample size and unequal variance. The sample size of the first and second class are calculated as the sum(w1) and sum(w2), respectively.

Value

A numeric vector of six values that are the difference between the two weighted means, the value of the t statistic, the sample size of class 1, the sample size of class 2, the degree of freedom and the corresponding p-value.

References

http://en.wikipedia.org/wiki/T_test

See Also

stats::weighted.mean

Examples

set.seed(54321)
# random generation of 50 normally distributed values for each of the two classes
xx <- c(rnorm(50), rnorm(50)+1)
# fuzzy membership to class 1
ww1 <- runif(50) + 0.3
ww1[ww1 > 1] <- 1
ww1 <- c(ww1, 1 - ww1)
# fuzzy membership to class 2
ww2 <- 1 - ww1
# Welch's t test weighted by fuzzy membership to class 1 and 2
wt <- fuzzy.ttest(x=xx, w1=ww1, w2=ww2)
print(wt)
# Not run:
# permutation test to compute the null distribution of the weighted t statistic
wt <- wt[2]
rands <- t(sapply(1:1000, function(x,y) { return(sample(1:y)) }, y=length(xx)))
randst <- apply(rands, 1, function(x, xx, ww1, ww2)
{ return(fuzzy.ttest(x=xx, w1=ww1[x], w2=ww2[x])[2]) }, xx=xx, ww1=ww1, ww2=ww2)
ifelse(wt < 0, sum(randst <= wt), sum(randst >= wt)) / length(randst)
# End(Not run)

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the 70 genes prognosis profile (GENE70) as published by van't Veer et al. 2002.

Usage

gene70(data, annot, do.mapping = FALSE, mapping,
  std = c("none", "scale", "robust"), verbose = FALSE)

Arguments

Value

A list with items:

• score Continuous signature scores

• risk Binary risk classification, 1 being high risk and 0 being low risk.

• mapping Mapping used if necessary.

• probe If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data

References

L. J. van't Veer and H. Dai and M. J. van de Vijver and Y. D. He and A. A. Hart and M. Mao and H. L. Peterse and K. van der Kooy and M. J. Marton and A. T. Witteveen and G. J. Schreiber and R. M. Kerkhiven and C. Roberts and P. S. Linsley and R. Bernards and S. H. Friend (2002) "Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer", Nature, 415:530–536.

See Also

nkis

Examples

# load GENE70 signature
data(sig.gene70)
# load NKI dataset
data(nkis)
# compute relapse score
rs.nkis <- gene70(data=data.nkis)
table(rs.nkis$risk)
# note that the discrepancies compared to the original publication
# are closed to the official cutoff, raising doubts on its exact value.
# computation of the signature scores on a different microarray platform
# load VDX dataset
data(vdxs)
# compute relapse score
rs.vdxs <- gene70(data=data.vdxs, annot=annot.vdxs, do.mapping=TRUE)
table(rs.vdxs$risk)

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the Relapse Score (GENE76) as published by Wang et al. 2005.

Usage

gene76(data, er)

Arguments

Value

A list with items:

• score Continuous signature scores

• risk Binary risk classification, 1 being high risk and 0 being low risk.

References

Y. Wang and J. G. Klijn and Y. Zhang and A. M. Sieuwerts and M. P. Look and F. Yang and D. Talantov and M. Timmermans and M. E. Meijer-van Gelder and J. Yu and T. Jatkoe and E. M. Berns and D. Atkins and J. A. Foekens (2005) "Gene-Expression Profiles to Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer", Lancet, 365(9460):671–679.

See Also

ggi

Examples

# load GENE76 signature
data(sig.gene76)
# load VDX dataset
data(vdxs)
# compute relapse score
rs.vdxs <- gene76(data=data.vdxs, er=demo.vdxs[ ,"er"])
table(rs.vdxs$risk)

Description

This function allows for fast mapping between two datasets or a dataset and a gene list. The mapping process is performed using Entrez Gene id as reference. In case of ambiguities (several probes representing the same gene), the most variant probe is selected.

Usage

geneid.map(geneid1, data1, geneid2, data2, verbose = FALSE)

Arguments

Value

A list with items:

• geneid1 Mapped gene list from geneid1.

• data1 Mapped dataset from data1.

• geneid2 Mapped gene list from geneid2.

• data2 Mapped dataset from data2.

Note

It is mandatory that the names of geneid1 and geneid2 must be the probe names of the microarray platform.

Examples

# load NKI data
data(nkis)
nkis.gid <- annot.nkis[ ,"EntrezGene.ID"]
names(nkis.gid) <- dimnames(annot.nkis)[[1]]
# load GGI signature
data(sig.ggi)
ggi.gid <- sig.ggi[ ,"EntrezGene.ID"]
names(ggi.gid) <- as.character(sig.ggi[ ,"probe"])
# mapping through Entrez Gene ids of NKI and GGI signature
res <- geneid.map(geneid1=nkis.gid, data1=data.nkis,
  geneid2=ggi.gid, verbose=FALSE)
str(res)

Description

This function computes the Gene Expression progNostic Index Using Subtypes (GENIUS) as published by Haibe-Kains et al. 2010. Subtype-specific risk scores are computed for each subtype signature separately and an overall risk score is computed by combining these scores with the posterior probability to belong to each of the breast cancer molecular subtypes.

Usage

genius(data, annot, do.mapping = FALSE, mapping, do.scale = TRUE)

Arguments

Value

A list with items:

• GENIUSM1: Risk score from the ER-/HER2- subtype signature in GENIUS model.

• GENIUSM2: Risk score from the HER2+ subtype signature in GENIUS model.

• GENIUSM3: Risk score from the ER+/HER2- subtype signature in GENIUS model.

• score: Overall risk prediction as computed by the GENIUS model.a.

References

Haibe-Kains B, Desmedt C, Rothe F, Sotiriou C and Bontempi G (2010) "A fuzzy gene expression-based computational approach improves breast cancer prognostication", Genome Biology, 11(2):R18

See Also

subtype.cluster.predict,sig.score

Examples

# load NKI dataset
data(nkis)
data(scmod1.robust)
data(sig.genius)

# compute GENIUS risk scores based on GENIUS model fitted on VDX dataset
genius.nkis <- genius(data=data.nkis, annot=annot.nkis, do.mapping=TRUE)
str(genius.nkis)
# the performance of GENIUS overall risk score predictions are not optimal
# since only part of the NKI dataset was used

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the Gene expression Grade Index (GGI).

Usage

ggi(data, annot, do.mapping = FALSE, mapping, hg, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Sotiriou C, Wirapati P, Loi S, Harris A, Bergh J, Smeds J, Farmer P, Praz V, Haibe-Kains B, Lallemand F, Buyse M, Piccart MJ and Delorenzi M (2006) "Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis", Journal of National Cancer Institute, 98:262–272

See Also

gene76

Examples

# load GGI signature
data(sig.ggi)
# load NKI dataset
data(nkis)
# compute relapse score
ggi.nkis <- ggi(data=data.nkis, annot=annot.nkis, do.mapping=TRUE,
  hg=demo.nkis[ ,"grade"])
table(ggi.nkis$risk)

Description

This function computes the prognostic score based on four measured IHC markers (ER, PGR, HER2, Ki-67), following the algorithm as published by Cuzick et al. 2011. The user has the option to either obtain just the shrinkage-adjusted IHC4 score (IHC4) or the overall score htat also combines the clinical score (IHC4+C)

Usage

ihc4(ER, PGR, HER2, Ki67,age,size,grade,node,ana,scoreWithClinical=FALSE, na.rm = FALSE)

Arguments

Value

Shrinkage-adjusted IHC4 score or the Overall Prognostic Score based on IHC4+C (IHC4+Clinical Score)

References

Jack Cuzick, Mitch Dowsett, Silvia Pineda, Christopher Wale, Janine Salter, Emma Quinn, Lila Zabaglo, Elizabeth Mallon, Andrew R. Green, Ian O. Ellis, Anthony Howell, Aman U. Buzdar, and John F. Forbes (2011) "Prognostic Value of a Combined Estrogen Receptor, Progesterone Receptor, Ki-67, and Human Epidermal Growth Factor Receptor 2 Immunohistochemical Score and Comparison with the Genomic Health Recurrence Score in Early Breast Cancer", Journal of Clinical Oncologoy, 29(32):4273–4278.

Examples

# load NKI dataset
data(nkis)
# compute shrinkage-adjusted IHC4 score
count<-nrow(demo.nkis)
ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
scoreWithClinical=FALSE, na.rm=TRUE)

# compute IHC4+C score
ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
age=demo.nkis[,"age"],size=demo.nkis[ ,"size"],grade=demo.nkis[ ,"grade"],node=demo.nkis[ ,"node"],
ana=sample(x=0:1,size=count,replace=TRUE), scoreWithClinical=TRUE, na.rm=TRUE)

Description

This function fits the Single Sample Predictor (SSP) as published in Sorlie et al 2003, Hu et al 2006 and Parker et al 2009. This model is actually a nearest centroid classifier where the centroids representing the breast cancer molecular subtypes are identified through hierarchical clustering using an "intrinsic gene list".

Usage

intrinsic.cluster(data, annot, do.mapping = FALSE, mapping,
  std = c("none", "scale", "robust"), rescale.q = 0.05, intrinsicg,
  number.cluster = 3, mins = 5, method.cor = c("spearman", "pearson"),
  method.centroids = c("mean", "median", "tukey"), filen, verbose = FALSE)

Arguments

Value

A list with items:

• model:Single Sample Predictor

• subtype: Subtypes identified by the SSP. For published intrinsic gene lists, subtypes can be either "Basal", "Her2", "LumA", "LumB" or "Normal".

• subtype.proba: Probabilities to belong to each subtype estimated from the correlations to each centroid.

• cor: Correlation coefficient to each centroid.

References

T. Sorlie and R. Tibshirani and J. Parker and T. Hastie and J. S. Marron and A. Nobel and S. Deng and H. Johnsen and R. Pesich and S. Geister and J. Demeter and C. Perou and P. E. Lonning and P. O. Brown and A. L. Borresen-Dale and D. Botstein (2003) "Repeated Observation of Breast Tumor Subtypes in Independent Gene Expression Data Sets", Proceedings of the National Academy of Sciences, 1(14):8418-8423 Hu, Zhiyuan and Fan, Cheng and Oh, Daniel and Marron, JS and He, Xiaping and Qaqish, Bahjat and Livasy, Chad and Carey, Lisa and Reynolds, Evangeline and ressler, Lynn and Nobel, Andrew and Parker, Joel and Ewend, Matthew and Sawyer, Lynda and Wu, Junyuan and Liu, Yudong and Nanda, Rita and Tretiakova, Maria and Orrico, Alejandra and Dreher, Donna and Palazzo, Juan and Perreard, Laurent and Nelson, Edward and Mone, Mary and Hansen, Heidi and Mullins, Michael and Quackenbush, John and Ellis, Matthew and Olopade, Olufunmilayo and Bernard, Philip and Perou, Charles (2006) "The molecular portraits of breast tumors are conserved across microarray platforms", BMC Genomics, 7(96) Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160-1167

See Also

subtype.cluster, intrinsic.cluster.predict, ssp2003, ssp2006, pam50

Examples

# load SSP signature published in Sorlie et al. 2003
data(ssp2003)
# load NKI data
data(nkis)
# load VDX data
data(vdxs)
ssp2003.nkis <- intrinsic.cluster(data=data.nkis, annot=annot.nkis,
  do.mapping=TRUE, std="robust",
  intrinsicg=ssp2003$centroids.map[ ,c("probe", "EntrezGene.ID")],
  number.cluster=5, mins=5, method.cor="spearman",
  method.centroids="mean", verbose=TRUE)
str(ssp2003.nkis, max.level=1)

Description

This function identifies the breast cancer molecular subtypes using a Single Sample Predictor (SSP) fitted by intrinsic.cluster.

Usage

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

Arguments

Value

A list with items:

• subtype: Subtypes identified by the SSP. For published intrinsic gene lists, subtypes can be either "Basal", "Her2", "LumA", "LumB" or "Normal".

• subtype.proba: Probabilities to belong to each subtype estimated from the correlations to each centroid.

• cor: Correlation coefficient to each centroid.

• prediction.strength: Prediction strength for subtypes.

• subtype.train: Classification (similar to subtypes) computed during fitting of the model for prediction strength.

• centroids.map: Mapped probes from the intrinsic gene list used to compute the centroids.

• profiles: Intrinsic gene expression profiles for each sample.

References

T. Sorlie and R. Tibshirani and J. Parker and T. Hastie and J. S. Marron and A. Nobel and S. Deng and H. Johnsen and R. Pesich and S. Geister and J. Demeter and C. Perou and P. E. Lonning and P. O. Brown and A. L. Borresen-Dale and D. Botstein (2003) "Repeated Observation of Breast Tumor Subtypes in Independent Gene Expression Data Sets", Proceedings of the National Academy of Sciences, 1(14):8418–8423 Hu, Zhiyuan and Fan, Cheng and Oh, Daniel and Marron, JS and He, Xiaping and Qaqish, Bahjat and Livasy, Chad and Carey, Lisa and Reynolds, Evangeline and Dressler, Lynn and Nobel, Andrew and Parker, Joel and Ewend, Matthew and Sawyer, Lynda and Wu, Junyuan and Liu, Yudong and Nanda, Rita and Tretiakova, Maria and Orrico, Alejandra and Dreher, Donna and Palazzo, Juan and Perreard, Laurent and Nelson, Edward and Mone, Mary and Hansen, Heidi and Mullins, Michael and Quackenbush, John and Ellis, Matthew and Olopade, Olufunmilayo and Bernard, Philip and Perou, Charles (2006) "The molecular portraits of breast tumors are conserved across microarray platforms", BMC Genomics, 7(96) Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160–1167 Tibshirani R and Walther G (2005) "Cluster Validation by Prediction Strength", Journal of Computational and Graphical Statistics, 14(3):511–528

See Also

intrinsic.cluster, ssp2003, ssp2006, pam50

Examples

# load SSP fitted in Sorlie et al. 2003
data(ssp2003)
# load NKI data
data(nkis)
# SSP2003 applied on NKI
ssp2003.nkis <- intrinsic.cluster.predict(sbt.model=ssp2003,
  data=data.nkis, annot=annot.nkis, do.mapping=TRUE,
  do.prediction.strength=FALSE, verbose=TRUE)
table(ssp2003.nkis$subtype)

Description

This function maps a list of datasets through EntrezGene IDs in order to get the union of the genes.

Usage

map.datasets(datas, annots, do.mapping = FALSE, 
  mapping.coln = "EntrezGene.ID", mapping, verbose = FALSE)

Arguments

Details

In case of several probes representing the same EntrezGene ID, the most variant is selected if mapping is not specified. When a EntrezGene ID does not exist in a specific dataset, NA values are introduced.

Value

A list with items:

• datas: List of datasets (gene expression matrices)

• annots: List of annotations (annotation matrices)

Examples

# load VDX dataset
data(vdxs)
# load NKI dataset
data(nkis)
# reduce datasets
ginter <- intersect(annot.vdxs[ ,"EntrezGene.ID"], annot.nkis[ ,"EntrezGene.ID"])
ginter <- ginter[!is.na(ginter)][1:30]
myx <- unique(c(match(ginter, annot.vdxs[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.vdxs), size=20)))
data2.vdxs <- data.vdxs[ ,myx]
annot2.vdxs <- annot.vdxs[myx, ]
myx <- unique(c(match(ginter, annot.nkis[ ,"EntrezGene.ID"]),
  sample(x=1:nrow(annot.nkis), size=20)))
data2.nkis <- data.nkis[ ,myx]
annot2.nkis <- annot.nkis[myx, ]
# mapping of datasets
datas <- list("VDX"=data2.vdxs,"NKI"=data2.nkis)
annots <- list("VDX"=annot2.vdxs, "NKI"=annot2.nkis)
datas.mapped <- map.datasets(datas=datas, annots=annots, do.mapping=TRUE)
str(datas.mapped, max.level=2)

Description

Utility function called within the claudinLow classifier

Usage

medianCtr(x)

Arguments

Value

A matrix of median-centered numbers

References

citation("claudinLow")

See Also

claudinLow

Description

List of seven gene modules published in Desmedt et a. 2008, i.e. ESR1 (estrogen receptor pathway), ERBB2 (her2/neu receptor pathway), AURKA (proliferation), STAT1 (immune response), PLAU (tumor invasion), VEGF (angogenesis) and CASP3 (apoptosis).

Usage

data(mod1)

Details

mod1 is a list of seven gene signatures, i.e. matrices with 3 columns containing the annotations and information related to the signatures themselves.

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.

Description

List of seven gene modules published in Wirapati et a. 2008, i.e. ESR1 (estrogen receptor pathway), ERBB2 (her2/neu receptor pathway) and AURKA (proliferation).

Usage

data(mod2)

Details

mod2 is a list of three gene signatures, i.e. matrices with 3 columns containing the annotations and information related to the signatures themselves.

Source

http://breast-cancer-research.com/content/10/4/R65

References

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.

Description

Object containing the set of parameters for the mixture of Gaussians used as a model to classify ovarian tumors into Angiogenic and NonAngiogenic subtypes.

Usage

data(modelOvcAngiogenic)

Source

[http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1](http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1)

References

Bentink S, Haibe-Kains B, Risch T, Fan J-B, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane AC, Drapkin R, Quackenbush JF, Matulonis UA (2012) "Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer", PloS one, 7(2):e30269

Description

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

Usage

molecular.subtyping(sbt.model = c("scmgene", "scmod1", "scmod2",
  "pam50", "ssp2006", "ssp2003", "intClust", "AIMS","claudinLow"),
  data, annot, do.mapping = FALSE, verbose = FALSE)

Arguments

Value

A list with items:

• subtype: Subtypes identified by the subtyping classification model.

• subtype.proba: Probabilities to belong to each subtype estimated by the subtyping classification model.

• subtype.crisp: Crisp classes identified by the subtyping classification model.

References

T. Sorlie and R. Tibshirani and J. Parker and T. Hastie and J. S. Marron and A. Nobel and S. Deng and H. Johnsen and R. Pesich and S. Geister and J. Demeter and C. Perou and P. E. Lonning and P. O. Brown and A. L. Borresen-Dale and D. Botstein (2003) "Repeated Observation of Breast Tumor Subtypes in Independent Gene Expression Data Sets", Proceedings of the National Academy of Sciences, 1(14):8418-8423 Hu, Zhiyuan and Fan, Cheng and Oh, Daniel and Marron, JS and He, Xiaping and Qaqish, Bahjat and Livasy, Chad and Carey, Lisa and Reynolds, Evangeline and Dressler, Lynn and Nobel, Andrew and Parker, Joel and Ewend, Matthew and Sawyer, Lynda and Wu, Junyuan and Liu, Yudong and Nanda, Rita and Tretiakova, Maria and Orrico, Alejandra and Dreher, Donna and Palazzo, Juan and Perreard, Laurent and Nelson, Edward and Mone, Mary and Hansen, Heidi and Mullins, Michael and Quackenbush, John and Ellis, Matthew and Olopade, Olufunmilayo and Bernard, Philip and Perou, Charles (2006) "The molecular portraits of breast tumors are conserved across microarray platforms", BMC Genomics, 7(96) Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160-1167 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. Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, Sotiriou C. (2012) "A three-gene model to robustly identify breast cancer molecular subtypes.", J Natl Cancer Inst., 104(4):311-325. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, Speed D, Lynch AG, Samarajiwa S, Yuan Y, Graf S, Ha G, Haffari G, Bashashati A, Russell R, McKinney S; METABRIC Group, Langerod A, Green A, Provenzano E, Wishart G, Pinder S, Watson P, Markowetz F, Murphy L, Ellis I, Purushotham A, Borresen-Dale AL, Brenton JD, Tavare S, Caldas C, Aparicio S. (2012) "The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.", Nature, 486(7403):346-352. Paquet ER, Hallett MT. (2015) "Absolute assignment of breast cancer intrinsic molecular subtype.", J Natl Cancer Inst., 107(1):357. Aleix Prat, Joel S Parker, Olga Karginova, Cheng Fan, Chad Livasy, Jason I Herschkowitz, Xiaping He, and Charles M. Perou (2010) "Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer", Breast Cancer Research, 12(5):R68

See Also

subtype.cluster.predict, intrinsic.cluster.predict

Examples

##### without mapping (affy hgu133a or plus2 only)
# load VDX data
data(vdxs)
data(AIMSmodel)
data(scmgene.robust)

# Subtype Clustering Model fitted on EXPO and applied on VDX
sbt.vdx.SCMGENE <- molecular.subtyping(sbt.model="scmgene",
  data=data.vdxs, annot=annot.vdxs, do.mapping=FALSE)
table(sbt.vdx.SCMGENE$subtype)

# Using the AIMS molecular subtyping algorithm
sbt.vdxs.AIMS <- molecular.subtyping(sbt.model="AIMS", data=data.vdxs,
                                     annot=annot.vdxs, do.mapping=FALSE)
table(sbt.vdxs.AIMS$subtype)

# Using the IntClust molecular subtyping algorithm
colnames(annot.vdxs)[3]<-"Gene.Symbol"
sbt.vdxs.intClust <- molecular.subtyping(sbt.model="intClust", data=data.vdxs,
  annot=annot.vdxs, do.mapping=FALSE)
table(sbt.vdxs.intClust$subtype)

##### with mapping
# load NKI data
data(nkis)

# Subtype Clustering Model fitted on EXPO and applied on NKI
sbt.nkis <- molecular.subtyping(sbt.model="scmgene", data=data.nkis,
  annot=annot.nkis, do.mapping=TRUE)
table(sbt.nkis$subtype)

##### with mapping
## load vdxs data
data(vdxs)
data(claudinLowData)

## Claudin-Low classification of 150 VDXS samples
sbt.vdxs.CL <- molecular.subtyping(sbt.model="claudinLow", data=data.vdxs,
  annot=annot.vdxs, do.mapping=TRUE)
table(sbt.vdxs.CL$subtype)

Description

This dataset contains (part of) the gene expression, annotations and clinical data as published in van de Vijver et al. 2002.

Usage

data(nkis)

Format

nkis is a dataset containing three matrices:

• data.nkis: Matrix containing gene expressions as measured by Agilent technology (dual-channel, oligonucleotides)

• annot.nkis: Matrix containing annotations of Agilent microarray platform

• demon.nkis: Clinical information of the breast cancer patients whose tumors were hybridized

Details

This dataset represent only partially the one published by van de Vijver et al. in 2008. Indeed, only part of the patients (150) and gene expressions (922) in data.nkis.

Source

http://www.nature.com/nature/journal/v415/n6871/full/415530a.html

References

M. J. van de Vijver and Y. D. He and L. van't Veer and H. Dai and A. M. Hart and D. W. Voskuil and G. J. Schreiber and J. L. Peterse and C. Roberts and M. J. Marton and M. Parrish and D. Atsma and A. Witteveen and A. Glas and L. Delahaye and T. van der Velde and H. Bartelink and S. Rodenhuis and E. T. Rutgers and S. H. Friend and R. Bernards (2002) "A Gene Expression Signature as a Predictor of Survival in Breast Cancer", New England Journal of Medicine, 347(25):1999–2009

Description

This function computes the Nottingham Prognostic Index (NPI) as published in Galeat et al, 1992. NPI is a clinical index shown to be highly prognostic in breast cancer.

Usage

npi(size, grade, node, na.rm = FALSE)

Arguments

Details

The risk prediction is either Good if score < 3.4, Intermediate if 3.4 <= score <- 5.4, or Poor if score > 5.4.

Value

A list with items:

• score: Continuous signature scores

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

References

Galea MH, Blamey RW, Elston CE, and Ellis IO (1992) "The nottingham prognostic index in primary breast cancer", Breast Cancer Reasearch and Treatment, 22(3):207-219.

See Also

st.gallen

Examples

# load NKI dataset
data(nkis)
# compute NPI score and risk classification
npi(size=demo.nkis[ ,"size"], grade=demo.nkis[ ,"grade"],
  node=ifelse(demo.nkis[ ,"node"] == 0, 1, 3), na.rm=TRUE)

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the OncotypeDX signature as published by Paik et al. 2004.

Usage

oncotypedx(data, annot, do.mapping = FALSE, mapping, do.scaling=TRUE,
  verbose = FALSE)

Arguments

Details

Note that for Affymetrix HGU datasets, the mapping is not necessary.

Value

A list with items:

• score: Continuous signature scores

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

S. Paik, S. Shak, G. Tang, C. Kim, J. Bakker, M. Cronin, F. L. Baehner, M. G. Walker, D. Watson, T. Park, W. Hiller, E. R. Fisher, D. L. Wickerham, J. Bryant, and N. Wolmark (2004) "A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer", New England Journal of Medicine, 351(27):2817-2826.

Examples

# load GENE70 signature
data(sig.oncotypedx)
# load NKI dataset
data(nkis)
# compute relapse score
rs.nkis <- oncotypedx(data=data.nkis, annot=annot.nkis, do.mapping=TRUE)
table(rs.nkis$risk)

Description

This function computes subtype scores and risk classifications from gene expression values following the algorithm developed by Bentink, Haibe-Kains et al. to identify the angiogenic molecular subtype in ovarian cancer.

Usage

ovcAngiogenic(data, annot, hgs,
gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene"),
do.mapping = FALSE, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores.

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

• subtype: data frame reporting the subtype score, maximum likelihood classification and corresponding subtype probabilities.

References

Bentink S, Haibe-Kains B, Risch T, Fan J-B, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane AC, Drapkin R, Quackenbush JF, Matulonis UA (2012) "Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer", PloS one, 7(2):e30269

See Also

sigOvcAngiogenic

Examples

# load the ovcAngiogenic signature

# load NKI dataset
data(nkis)
colnames(annot.nkis)[is.element(colnames(annot.nkis), "EntrezGene.ID")] <- 
  "entrezgene"

# compute relapse score
ovcAngiogenic.nkis <- ovcAngiogenic(data=data.nkis, annot=annot.nkis,
  gmap="entrezgene", do.mapping=TRUE)
table(ovcAngiogenic.nkis$risk)

Description

This function computes subtype scores and risk classifications from gene expression values using the weights published by Crijns et al.

Usage

ovcCrijns(data, annot, hgs,
  gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene"),
  do.mapping = FALSE, verbose = FALSE)

Arguments

Details

Note that the original algorithm has not been implemented as it necessitates refitting of the model weights in each new dataset. However the current implementation should give similar results.

Value

A list with items:

• score: Continuous signature scores.

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence. between the gene list (aka signature) and gene expression data.

References

Crijns APG, Fehrmann RSN, de Jong S, Gerbens F, Meersma G J, Klip HG, Hollema H, Hofstra RMW, te Meerman GJ, de Vries EGE, van der Zee AGJ (2009) "Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer" PLoS Medicine, 6(2):e1000024.

See Also

sigOvcCrijns

Examples

# load the ovsCrijns signature
data(sigOvcCrijns)
# load NKI dataset
data(nkis)
colnames(annot.nkis)[is.element(colnames(annot.nkis), "EntrezGene.ID")] <- 
  "entrezgene"
# compute relapse score
ovcCrijns.nkis <- ovcCrijns(data=data.nkis, annot=annot.nkis, 
  gmap="entrezgene", do.mapping=TRUE)
table(ovcCrijns.nkis$risk)

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm developed by the TCGA consortium for ovarian cancer.

Usage

ovcTCGA(data, annot,
  gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene"),
  do.mapping = FALSE, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores.

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Bell D, Berchuck A, Birrer M et al. (2011) "Integrated genomic analyses of ovarian carcinoma", Nature, 474(7353):609-615

See Also

sigOvcTCGA

Examples

# load the ovcTCGA signature
data(sigOvcTCGA)
# load NKI dataset
data(nkis)
colnames(annot.nkis)[is.element(colnames(annot.nkis), "EntrezGene.ID")] <- "entrezgene"
# compute relapse score
ovcTCGA.nkis <- ovcTCGA(data=data.nkis, annot=annot.nkis, gmap="entrezgene", do.mapping=TRUE)
table(ovcTCGA.nkis$risk)

Description

This function computes subtype scores and risk classifications from gene expression values following the algorithm developed by Yoshihara et al, for prognosis in ovarian cancer.

Usage

ovcYoshihara(data, annot, hgs,
  gmap = c("entrezgene", "ensembl_gene_id", "hgnc_symbol", "unigene", "refseq_mrna"),
  do.mapping = FALSE, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores.

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, Onishi Y, Hatae M, Sueyoshi K, Fujiwara H, Kudo, Yoshiki, Kotera K, Masuzaki H, Tashiro H, Katabuchi H, Inoue I, Tanaka K (2010) "Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets", PloS one, 5(3):e9615.

See Also

sigOvcYoshihara

Examples

# load the ovcYoshihara signature
data(sigOvcYoshihara)
# load NKI dataset
data(nkis)
colnames(annot.nkis)[is.element(colnames(annot.nkis), "EntrezGene.ID")] <- "entrezgene"
# compute relapse score
ovcYoshihara.nkis <- ovcYoshihara(data=data.nkis,
  annot=annot.nkis, gmap="entrezgene", do.mapping=TRUE)
table(ovcYoshihara.nkis$risk)

Description

Utility function called within the claudinLow classifien.

Usage

overlapSets(x,y)

Arguments

Value

A list of overlapped dataset

References

citation("claudinLow")

See Also

claudinLow

Description

List of parameters defining the PAM50 classifier for identification of breast cancer molecular subtypes (Parker et al 2009).

Usage

data(pam50)
data(pam50.scale)
data(pam50.robust)

Format

List of parameters for PAM50:

• centroids: Gene expression centroids for each subtype.

• centroids.map: Mapping for centroids.

• method.cor: Method of correlation used to compute distance to the centroids.

• method.centroids: Method used to compute the centroids.

• std: Method of standardization for gene expressions ("none", "scale" or "robust")

• mins: Minimum number of samples within each cluster allowed during the fitting of the model.

Details

Three versions of the model are provided, each of ones differs by the gene expressions standardization method since it has an important impact on the subtype classification:

• pam50: Use of the official centroids without scaling of the gene expressions.

• pam50.scale: Use of the official centroids with traditional scaling of the gene expressions (see base::scale())

• pam50.robust: Use of the official centroids with robust scaling of the gene expressions (see rescale()) The model 'pam50.robust“ has been shown to reach the best concordance with the traditional clinical parameters (ER IHC, HER2 IHC/FISH and histological grade). However the use of this model is recommended only when the dataset is representative of a global population of breast cancer patients (no sampling bias, the 5 subtypes should be present).

Source

http://jco.ascopubs.org/cgi/content/short/JCO.2008.18.1370v1

References

Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160–1167

Description

This function computes signature scores from gene expression values following the algorithm used for the PIK3CA gene signature (PIK3CA-GS).

Usage

pik3cags(data, annot, do.mapping = FALSE, mapping, verbose = FALSE)

Arguments

Value

Vector of signature scores for PIK3CA-GS

References

Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, Gonzalez-Angulo AM, Pusztai L, Symmans FW, Bardelli A, Ellis P, Tutt AN, Gillett CE, Hennessy BT., Mills GB, Phillips WA, Piccart MJ, Speed TP, McArthur GA, Sotiriou C (2010) "PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer", Proceedings of the National Academy of Sciences, 107(22):10208-10213

See Also

gene76

Examples

# load GGI signature
data(sig.pik3cags)
# load NKI dataset
data(nkis)
# compute relapse score
pik3cags.nkis <- pik3cags(data=data.nkis, annot=annot.nkis, do.mapping=TRUE)
head(pik3cags.nkis)

Description

This function enables to compute the sample size requirements for estimating pearson, kendall and spearman correlations

Usage

power.cor(rho, w, alpha = 0.05, method = c("pearson", "kendall", "spearman"))

Arguments

Value

sample size requirement

References

Bonett, D. G., and Wright, T. A. (2000). Sample size requirements for estimating pearson, kendall and spearman correlations. Psychometrika, 65(1), 23-28. doi:10.1007/BF02294183

Examples

power.cor(rho=0.5, w=0.1, alpha=0.05, method="spearman")

Description

This function computes the prediction strength of a clustering model as published in R. Tibshirani and G. Walther 2005.

Usage

ps.cluster(cl.tr, cl.ts, na.rm = FALSE)

Arguments

Value

A list with items:

• ps: the overall prediction strength (minimum of the prediction strengths at cluster level).

• ps.cluster: Prediction strength for each cluster

• ps.individual: Prediction strength for each sample.

References

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

Examples

# load SSP signature published in Sorlie et al. 2003
data(ssp2003)
# load NKI data
data(nkis)
# SP2003 fitted on NKI
ssp2003.2nkis <- intrinsic.cluster(data=data.nkis, annot=annot.nkis,
  do.mapping=TRUE, std="robust",
  intrinsicg=ssp2003$centroids.map[ ,c("probe", "EntrezGene.ID")],
  number.cluster=5, mins=5, method.cor="spearman",
  method.centroids="mean", verbose=TRUE)
# SP2003 published in Sorlie et al 2003 and applied in VDX
ssp2003.nkis <- intrinsic.cluster.predict(sbt.model=ssp2003,
  data=data.nkis, annot=annot.nkis, do.mapping=TRUE, verbose=TRUE)
# prediction strength of sp2003 clustering model
ps.cluster(cl.tr=ssp2003.2nkis$subtype, cl.ts=ssp2003.nkis$subtype,
  na.rm = FALSE)

Description

This function allows for reading a 'csv' file containing gene signatures. Each gene signature is composed of at least four columns: "gene.list" is the name of the signature on the first line and empty fields below, "probes" are the probe names, "EntrezGene.ID" are the EntrezGene IDs and "coefficient" are the coefficients of each probe.

Usage

read.m.file(file, ...)

Arguments

Value

List of gene signatures.

See Also

mod1, mod2, 'extdata/desmedt2008_genemodules.csv', 'extdata/haibekains2009_sig_genius.csv'

Examples

# read the seven gene modules as published in Desmedt et al 2008
genemods <- read.m.file(system.file("extdata/desmedt2008_genemodules.csv",
  package = "genefu"))
str(genemods, max.level=1)
# read the three subtype signtaures from GENIUS
geniusm <- read.m.file(system.file("extdata/haibekains2009_sig_genius.csv",
  package = "genefu"))
str(geniusm, max.level=1)

Description

Formatting function to read arrays and format for use in the claudinLow classifier.

Usage

readArray(dataFile,designFile=NA,hr=1,impute=TRUE,method="mean")

Arguments

Value

A list

References

citation("claudinLow")

See Also

claudinLow

Description

This function renames duplicated strings by adding their number of occurrences at the end.

Usage

rename.duplicate(x, sep = "_", verbose = FALSE)

Arguments

Value

A list with items:

• new.x: new strings (without duplicates).

• duplicated.x: strings which were originally duplicated.

Examples

nn <- sample(letters[1:10], 30, replace=TRUE)
table(nn)
rename.duplicate(x=nn, verbose=TRUE)

Description

This function rescales values x based on quantiles specified by the user such that x' = (x - q1) / (q2 - q1) where q is the specified quantile, q1 = q / 2, q2 = 1 - q/2) and x' are the new rescaled values.

Usage

rescale(x, na.rm = FALSE, q = 0)

Arguments

Details

In order to rescale gene expressions, q = 0.05 yielded comparable scales in numerous breast cancer microarray datasets (data not shown).The rational behind this is that, in general, 'extreme cases' (e.g. low and high proliferation, high and low expression of ESR1, ...) are often present in microarray datasets, making the estimation of 'extreme' quantiles quite stable. This is specially true for genes exhibiting some multi-modality like ESR1 or ERBB2.

Value

A vector of rescaled values with two attributes q1 and q1 containing the values of the lower and the upper quantiles respectively.

See Also

base::scale()

Examples

# load VDX dataset
data(vdxs)
# load NKI dataset
data(nkis)
# example of rescaling for ESR1 expression
par(mfrow=c(2,2))
hist(data.vdxs[ ,"205225_at"], xlab="205225_at", breaks=20,
  main="ESR1 in VDX")
hist(data.nkis[ ,"NM_000125"], xlab="NM_000125", breaks=20,
  main="ESR1 in NKI")
hist((rescale(x=data.vdxs[ ,"205225_at"], q=0.05) - 0.5) * 2,
  xlab="205225_at", breaks=20, main="ESR1 in VDX\nrescaled")
hist((rescale(x=data.nkis[ ,"NM_000125"], q=0.05) - 0.5) * 2,
  xlab="NM_000125", breaks=20, main="ESR1 in NKI\nrescaled")

Description

This function computes signature scores and risk classifications from gene expression values following the algorithm used for the rorS signature as published by Parker et al 2009.

Usage

rorS(data, annot, do.mapping = FALSE, mapping, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores

• risk: Binary risk classification, 1 being high risk and 0 being low risk.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Parker, Joel S. and Mullins, Michael and Cheang, Maggie C.U. and Leung, Samuel and Voduc, David and Vickery, Tammi and Davies, Sherri and Fauron, Christiane and He, Xiaping and Hu, Zhiyuan and Quackenbush, John F. and Stijleman, Inge J. and Palazzo, Juan and Marron, J.S. and Nobel, Andrew B. and Mardis, Elaine and Nielsen, Torsten O. and Ellis, Matthew J. and Perou, Charles M. and Bernard, Philip S. (2009) "Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes", Journal of Clinical Oncology, 27(8):1160-1167

Examples

# load NKI dataset
data(vdxs)
data(pam50)

# compute relapse score
rs.vdxs <- rorS(data=data.vdxs, annot=annot.vdxs, do.mapping=TRUE)

Description

List of parameters defining the Subtype Clustering Model as published in Wirapati et al 2009 and Desmedt et al 2008 but using single genes instead of gene modules.

Usage

data(scmgene.robust)

Format

List of parameters for SCMGENE:

• parameters: List of parameters for the mixture of three Gaussians (ER-/HER2-, HER2+ and ER+/HER2-) that define the Subtype Clustering Model. The structure is the same than for an mclust::Mclust object.

• cutoff.AURKA: Cutoff for AURKA module score in order to identify ER+/HER2- High Proliferation (aka Luminal B) tumors and ER+/HER2- Low Proliferation (aka Luminal A) tumors.

• mod: ESR1, ERBB2 and AURKA modules.

Source

http://clincancerres.aacrjournals.org/content/14/16/5158.abstract?ck=nck

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.

Description

List of parameters defining the Subtype Clustering Model as published in Desmedt et al 2008.

Usage

data(scmod1.robust)

Format

List of parameters for SCMOD1:

• parameters: List of parameters for the mixture of three Gaussians (ER-/HER2-, HER2+ and ER+/HER2-) that define the Subtype Clustering Model. The structure is the same than for an mclust::Mclust() object.

• cutoff.AURKA: Cutoff for AURKA module score in order to identify ER+/HER2- High Proliferation (aka Luminal B) tumors and ER+/HER2- Low Proliferation (aka Luminal A) tumors.

• mod: ESR1, ERBB2 and AURKA modules.

Source

http://clincancerres.aacrjournals.org/content/14/16/5158.abstract?ck=nck

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.

Description

List of parameters defining the Subtype Clustering Model as published in Desmedt et al 2008.

Usage

data(scmod1.robust)

Format

List of parameters for SCMOD2:

• parameters: List of parameters for the mixture of three Gaussians (ER-/HER2-, HER2+ and ER+/HER2-) that define the Subtype Clustering Model. The structure is the same than for an mclust::Mclust object.

• cutoff.AURKA: Cutoff for AURKA module score in order to identify ER+/HER2- High Proliferation (aka Luminal B) tumors and ER+/HER2- Low Proliferation (aka Luminal A) tumors.

• mod: ESR1, ERBB2 and AURKA modules.

Source

http://breast-cancer-research.com/content/10/4/R65k

References

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.

Description

This function enables to set the class of each column in a data.frame.

Usage

setcolclass.df(df, colclass, factor.levels)

Arguments

Value

A data.frame with columns' class and levels properly set

Examples

tt <- data.frame(matrix(NA, nrow=3, ncol=3, dimnames=list(1:3, paste("column", 1:3, sep="."))), 
  stringsAsFactors=FALSE)
tt <- setcolclass.df(df=tt, colclass=c("numeric", "factor", "character"), 
  factor.levels=list(NULL, c("F1", "F2", "F3"), NULL))

Description

List of 11 genes included in the endoPredict signature. The EntrezGene.ID allows for mapping and the mapping to affy probes is already provided.

Usage

data(sig.endoPredict)

Format

sig.endoPredict is a matrix with 5 columns containing the annotations and information related to the signature itself (including a mapping to Affymetrix HGU platform).

References

Filipits, M., Rudas, M., Jakesz, R., Dubsky, P., Fitzal, F., Singer, C. F., et al. (2011). "A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors." Clinical Cancer Research, 17(18):6012–6020.

Description

List of 70 agilent probe ids representing 56 unique genes included in the GENE70 signature. The EntrezGene.ID allows for mapping and the "average.good.prognosis.profile" values allows for signature computation.

Usage

data(sig.gene70)

Format

sig.gene70 is a matrix with 9 columns containing the annotations and information related to the signature itself.

Source

http://www.nature.com/nature/journal/v415/n6871/full/415530a.html

References

L. J. van't Veer and H. Dai and M. J. van de Vijver and Y. D. He and A. A. Hart and M. Mao and H. L. Peterse and K. van der Kooy and M. J. Marton and A. T. Witteveen and G. J. Schreiber and R. M. Kerkhiven and C. Roberts and P. S. Linsley and R. Bernards and S. H. Friend (2002) "Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer", Nature, 415:530–536.

Description

List of 76 affymetrix hgu133a probesets representing 60 unique genes included in the GENE76 signature. The EntrezGene.ID allows for mapping and the coefficient allows for signature computation.

Usage

data(sig.gene76)

Format

sig.gene70 is a matrix with 10 columns containing the annotations and information related to the signature itself.

Source

http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(05)17947-1/abstract

References

Y. Wang and J. G. Klijn and Y. Zhang and A. M. Sieuwerts and M. P. Look and F. Yang and D. Talantov and M. Timmermans and M. E. Meijer-van Gelder and J. Yu and T. Jatkoe and E. M. Berns and D. Atkins and J. A. Foekens (2005) "Gene-Expression Profiles to Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer", Lancet, 365(9460):671–679.

Description

List of three gene signatures which compose the Gene Expression progNostic Index Using Subtypes (GENIUS) as published by Haibe-Kains et al. 2009. GENIUSM1, GENIUSM2 and GENIUSM3 are the ER-/HER2-, HER2+ and ER+/HER2- subtype signatures respectively.

Format

sig.genius is a list a three subtype signatures.

References

Haibe-Kains B, Desmedt C, Rothe F, Sotiriou C and Bontempi G (2010) "A fuzzy gene expression-based computational approach improves breast cancer prognostication", Genome Biology, 11(2):R18

Description

List of 128 affymetrix hgu133a probesets representing 97 unique genes included in the GGI signature. The "EntrezGene.ID" column allows for mapping and "grade" defines the up-regulation of the expressions either in histological grade 1 or 3.

Usage

data(sig.ggi)

Format

sig.ggi is a matrix with 9 columns containing the annotations and information related to the signature itself.

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Sotiriou C, Wirapati P, Loi S, Harris A, Bergh J, Smeds J, Farmer P, Praz V, Haibe-Kains B, Lallemand F, Buyse M, Piccart MJ and Delorenzi M (2006) "Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis", Journal of National Cancer Institute, 98:262–272

Description

List of 21 genes included in the OncotypeDX signature. The EntrezGene.ID allows for mapping and the mapping to affy probes is already provided.

Usage

data(sig.oncotypedx)

References

S. Paik, S. Shak, G. Tang, C. Kim, J. Bakker, M. Cronin, F. L. Baehner, M. G. Walker, D. Watson, T. Park, W. Hiller, E. R. Fisher, D. L. Wickerham, J. Bryant, and N. Wolmark (2004) "A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer", New England Journal of Medicine, 351(27):2817–2826.

Description

List of 278 affymetrix hgu133a probesets representing 236 unique genes included in the PIK3CA-GS signature. The "EntrezGene.ID" column allows for mapping and "coefficient" refers to to the direction of association with PIK3CA mutation.

Usage

data(sig.pik3cags)

Format

sig.pik3cags is a matrix with 3 columns containing the annotations and information related to the signature itself.

Source

http://www.pnas.org/content/107/22/10208/suppl/DCSupplemental

References

Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, Gonzalez-Angulo AM, Pusztai L, Symmans FW, Bardelli A, Ellis P, Tutt AN, Gillett CE, Hennessy BT., Mills GB, Phillips WA, Piccart MJ, Speed TP, McArthur GA, Sotiriou C (2010) "PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer", Proceedings of the National Academy of Sciences, 107(22):10208–10213

Description

This function computes a signature score from a gene list (aka gene signature), i.e. a signed average as published in Sotiriou et al. 2006 and Haibe-Kains et al. 2009.

Usage

sig.score(x, data, annot, do.mapping = FALSE, mapping, size = 0,
  cutoff = NA, signed = TRUE, verbose = FALSE)

Arguments

Value

A list with items:

• score: Signature score.

• mapping: Mapping used if necessary.

• probe: If mapping is performed, this matrix contains the correspondence between the gene list (aka signature) and gene expression data.

References

Sotiriou C, Wirapati P, Loi S, Harris A, Bergh J, Smeds J, Farmer P, Praz V, Haibe-Kains B, Lallemand F, Buyse M, Piccart MJ and Delorenzi M (2006) "Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis", Journal of National Cancer Institute, 98:262-272 Haibe-Kains B (2009) "Identification and Assessment of Gene Signatures in Human Breast Cancer", PhD thesis at Universite Libre de Bruxelles, http://theses.ulb.ac.be/ETD-db/collection/available/ULBetd-02182009-083101/

Examples

# load NKI data
data(nkis)
# load GGI signature
data(sig.ggi)
# make of ggi signature a gene list
ggi.gl <- cbind(sig.ggi[ ,c("probe", "EntrezGene.ID")],
  "coefficient"=ifelse(sig.ggi[ ,"grade"] == 1, -1, 1))
# computation of signature scores
ggi.score <- sig.score(x=ggi.gl, data=data.nkis, annot=annot.nkis,
  do.mapping=TRUE, signed=TRUE, verbose=TRUE)
str(ggi.score)

Description

List of 13 clusters of genes (and annotations) and their corresponding coefficient as an additional attribute.

Usage

data(sig.tamr13)

Format

sig.tamr13 is a list a 13 clusters of genes with their corresponding coefficient.

References

Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet C, Ellis P, Ryder K, Reid JF, Daidone MG, Pierotti MA, Berns EMJJ, Jansen MPHM, Foekens JA, Delorenzi M, Bontempi G, Piccart MJ and Sotiriou C (2008) "Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen", BMC Genomics, 9(1):239

Description

sigOvcAngiogenic dataset

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Bentink S, Haibe-Kains B, Risch T, Fan J-B, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane AC, Drapkin R, Quackenbush JF, Matulonis UA (2012) "Angiogenic mRNA and microRNA Gene Expression Signature Predicts a Novel Subtype of Serous Ovarian Cancer", PloS one, 7(2):e30269

Description

sigOvcCrijns dataset

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Crijns APG, Fehrmann RSN, de Jong S, Gerbens F, Meersma G J, Klip HG, Hollema H, Hofstra RMW, te Meerman GJ, de Vries EGE, van der Zee AGJ (2009) "Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer" PLoS Medicine, 6(2):e1000024.

Description

sigOcvSpentzos dataset

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Spentzos, D., Levine, D. A., Ramoni, M. F., Joseph, M., Gu, X., Boyd, J., et al. (2004). "Gene expression signature with independent prognostic significance in epithelial ovarian cancer". Journal of clinical oncology, 22(23), 4700–4710. doi:10.1200/JCO.2004.04.070

Description

sigOvcTCGA dataset

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Bell D, Berchuck A, Birrer M et al. (2011) "Integrated genomic analyses of ovarian carcinoma", Nature, 474(7353):609–615

Description

sigOvcYoshihara dataset

Source

http://jnci.oxfordjournals.org/cgi/content/full/98/4/262/DC1

References

Yoshihara K, Tajima A, Yahata T, Kodama S, Fujiwara H, Suzuki M, Onishi Y, Hatae M, Sueyoshi K, Fujiwara H, Kudo, Yoshiki, Kotera K, Masuzaki H, Tashiro H, Katabuchi H, Inoue I, Tanaka K (2010) "Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets", PloS one, 5(3):e9615.

Description

This function enables to compute the confidence interval for the Spearman correelation coefficient using the Fischer Z transformation.

Usage

spearmanCI(x, n, alpha = 0.05)

Arguments

Value

A vector containing the lower, upper values for the confidence interval and p-value for Spearman rho

Examples

spearmanCI(x=0.2, n=100, alpha=0.05)

Description

List of parameters defining the SSP2003 classifier for identification of breast cancer molecular subtypes (Sorlie et al 2003).

Usage

data(ssp2003)
data(ssp2003.robust)
data(ssp2003.scale)

Format

List of parameters for SSP2003:

• centroids: Gene expression centroids for each subtype.

• centroids.map: Mapping for centroids.

• method.cor: Method of correlation used to compute distance to the centroids.

• method.centroids: Method used to compute the centroids.

• std: Method used to compute the centroids.

• mins: Minimum number of samples within each cluster allowed during the fitting of the model.

Source

http://www.pnas.org/content/100/14/8418

References

T. Sorlie and R. Tibshirani and J. Parker and T. Hastie and J. S. Marron and A. Nobel and S. Deng and H. Johnsen and R. Pesich and S. Geister and J. Demeter and C. Perou and P. E. Lonning and P. O. Brown and A. L. Borresen-Dale and D. Botstein (2003) "Repeated Observation of Breast Tumor Subtypes in Independent Gene Expression Data Sets", Proceedings of the National Academy of Sciences, 1(14):8418–8423

Description

List of parameters defining the SSP2006 classifier for identification of breast cancer molecular subtypes (Hu et al 2006).

Usage

data(ssp2006)
data(ssp2006.robust)
data(ssp2006.scale)

Format

List of parameters for SSP2006:

• centroids: Gene expression centroids for each subtype.

• centroids.map: Mapping for centroids.

• method.cor: Method of correlation used to compute distance to the centroids.

• method.centroids: Method used to compute the centroids.

• std: Method of standardization for gene expressions.

• mins: Minimum number of samples within each cluster allowed during the fitting of the model.

Details

Three versions of the model are provided, each of ones differs by the gene expressions standardization method since it has an important impact on the subtype classification:

• ssp2006: Use of the official centroids without scaling of the gene expressions.

• ssp2006.scale: Use of the official centroids with traditional scaling of the gene expressions (see base::scale())

• ssp2006.robust: Use of the official centroids with robust scaling of the gene expressions (see rescale()) The model ssp2006.robust has been shown to reach the best concordance with the traditional clinical parameters (ER IHC, HER2 IHC/FISH and histological grade). However the use of this model is recommended only when the dataset is representative of a global population of breast cancer patients (no sampling bias, the 5 subtypes should be present).

Source

http://www.biomedcentral.com/1471-2164/7/96

References

Hu, Zhiyuan and Fan, Cheng and Oh, Daniel and Marron, JS and He, Xiaping and Qaqish, Bahjat and Livasy, Chad and Carey, Lisa and Reynolds, Evangeline and Dressler, Lynn and Nobel, Andrew and Parker, Joel and Ewend, Matthew and Sawyer, Lynda and Wu, Junyuan and Liu, Yudong and Nanda, Rita and Tretiakova, Maria and Orrico, Alejandra and Dreher, Donna and Palazzo, Juan and Perreard, Laurent and Nelson, Edward and Mone, Mary and Hansen, Heidi and Mullins, Michael and Quackenbush, John and Ellis, Matthew and Olopade, Olufunmilayo and Bernard, Philip and Perou, Charles (2006) "The molecular portraits of breast tumors are conserved across microarray platforms", BMC Genomics, 7(96)

Description

This function computes the updated St Gallen consensus criterions as published by Goldhirsh et al 2003.

Usage

st.gallen(size, grade, node, her2.neu, age, vascular.inv, na.rm = FALSE)

Arguments

Value

Vector of risk predictions: "Good", "Intermediate", and "Poor".

References

Goldhirsh A, Wood WC, Gelber RD, Coates AS, Thurlimann B, and Senn HJ (2003) "Meeting highlights: Updated international expert consensus on the primary therapy of early breast cancer", Journal of Clinical Oncology, 21(17):3357-3365.

See Also

npi

Examples

# load nkis dataset
data(nkis)

# compute St Gallen predictions
st.gallen(size=demo.nkis[ ,"size"], grade=demo.nkis[ ,"grade"],
  node=demo.nkis[ ,"node"], her2.neu=sample(x=0:1, size=nrow(demo.nkis),
  replace=TRUE), age=demo.nkis[ ,"age"], vascular.inv=sample(x=0:1,                                                                                                           
  size=nrow(demo.nkis), replace=TRUE), na.rm=TRUE)

Description

This function computes several indexes to quantify feature selection stability. This is usually estimated through perturbation of the original dataset by generating multiple sets of selected features.

Usage

stab.fs(fsets, N, method = c("kuncheva", "davis"), ...)

Arguments

Details

Stability indices may use different parameters. In this version only the Davis index requires an additional parameter that is penalty, a numeric value used as penalty term. Kuncheva index (kuncheva) lays in [-1, 1], An index of -1 means no intersection between sets of selected features, +1 means that all the same features are always selected and 0 is the expected stability of a random feature selection. Davis index (davis) lays in [0,1], With a penalty term equal to 0, an index of 0 means no intersection between sets of selected features and +1 means that all the same features are always selected. A penalty of 1 is usually used so that a feature selection performed with no or all features has a Davis stability index equals to 0. None estimate of the expected Davis stability index of a random feature selection was published.

Value

A numeric that is the stability index.

References

Davis CA, Gerick F, Hintermair V, Friedel CC, Fundel K, Kuffner R, Zimmer R (2006) "Reliable gene signatures for microarray classification: assessment of stability and performance", Bioinformatics, 22(19):356-2363. Kuncheva LI (2007) "A stability index for feature selection", AIAP'07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference, pages 390-395.

See Also

stab.fs.ranking

Examples

set.seed(54321)
# 100 random selection of 50 features from a set of 10,000 features
fsets <- lapply(as.list(1:100), function(x, size=50, N=10000) {
  return(sample(1:N, size, replace=FALSE))} )
names(fsets) <- paste("fsel", 1:length(fsets), sep=".")

# Kuncheva index
stab.fs(fsets=fsets, N=10000, method="kuncheva")
# close to 0 as expected for a random feature selection

# Davis index
stab.fs(fsets=fsets, N=10000, method="davis", penalty=1)

Description

This function computes several indexes to quantify feature ranking stability for several number of selected features. This is usually estimated through perturbation of the original dataset by generating multiple sets of selected features.

Usage

stab.fs.ranking(fsets, sizes, N, method = c("kuncheva", "davis"), ...)

Arguments

Details

Stability indices may use different parameters. In this version only the Davis index requires an additional parameter that is penalty, a numeric value used as penalty term. Kuncheva index (kuncheva) lays in [-1, 1], An index of -1 means no intersection between sets of selected features, +1 means that all the same features are always selected and 0 is the expected stability of a random feature selection. Davis index (davis) lays in [0,1], With a penalty term equal to 0, an index of 0 means no intersection between sets of selected features and +1 means that all the same features are always selected. A penalty of 1 is usually used so that a feature selection performed with no or all features has a Davis stability index equals to 0. None estimate of the expected Davis stability index of a random feature selection was published.

Value

A vector of numeric that are stability indices for each size of the sets of selected features given the rankings.

References

Davis CA, Gerick F, Hintermair V, Friedel CC, Fundel K, Kuffner R, Zimmer R (2006) "Reliable gene signatures for microarray classification: assessment of stability and performance", Bioinformatics, 22(19):356-2363. Kuncheva LI (2007) "A stability index for feature selection", AIAP'07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference, pages 390-395.

See Also

stab.fs

Examples

# 100 random selection of 50 features from a set of 10,000 features
fsets <- lapply(as.list(1:100), function(x, size=50, N=10000) {
  return(sample(1:N, size, replace=FALSE))} )
names(fsets) <- paste("fsel", 1:length(fsets), sep=".")

# Kuncheva index
stab.fs.ranking(fsets=fsets, sizes=c(1, 10, 20, 30, 40, 50),
  N=10000, method="kuncheva")
# close to 0 as expected for a random feature selection

# Davis index
stab.fs.ranking(fsets=fsets, sizes=c(1, 10, 20, 30, 40, 50),
  N=10000, method="davis", penalty=1)

Description

This function returns a vector of strings in which LaTeX special characters are escaped, this was useful in conjunction with xtable.

Usage

strescR(strings)

Arguments

Value

A vector of strings with escaped characters within each string.

References

citation("seqinr")

See Also

stresc

Examples

strescR("MISC_RNA")
strescR(c("BB_0001","BB_0002"))

Description

This function fits the Subtype Clustering Model as published in Desmedt et al. 2008 and Wiarapati et al. 2008. This model is actually a mixture of three Gaussians with equal shape, volume and variance (see EEI model in Mclust). This model is adapted to breast cancer and uses ESR1, ERBB2 and AURKA dimensions to identify the molecular subtypes, i.e. ER-/HER2-, HER2+ and ER+/HER2- (Low and High Prolif).

Usage

subtype.cluster(module.ESR1, module.ERBB2, module.AURKA, data, annot,
  do.mapping = FALSE, mapping, do.scale = TRUE, rescale.q = 0.05,
  model.name = "EEI", do.BIC = FALSE, plot = FALSE, filen, verbose = FALSE)

Arguments

Value

A list with items:

• model: Subtype Clustering Model (mixture of three Gaussians), like scmgene.robust, scmod1.robust and scmod2.robust when this function is used on expO dataset (International Genomics Consortium) with the gene modules published in the two references cited below.

• BIC: Bayesian Information Criterion for the Subtype Clustering Model with number of clusters ranging from 1 to 10.

• 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.

• 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.

• module.scores: Matrix containing ESR1, ERBB2 and AURKA module scores.

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.

See Also

subtype.cluster.predict, intrinsic.cluster, intrinsic.cluster.predict, scmod1.robust, scmod2.robust

Examples

# example without gene mapping
# load expO data
data(expos)
# load gene modules
data(mod1)
# fit a Subtype Clustering Model
scmod1.expos <- subtype.cluster(module.ESR1=mod1$ESR1, module.ERBB2=mod1$ERBB2,
  module.AURKA=mod1$AURKA, data=data.expos, annot=annot.expos, do.mapping=FALSE,
  do.scale=TRUE, plot=TRUE, verbose=TRUE)
str(scmod1.expos, max.level=1)
table(scmod1.expos$subtype2)

# example with gene mapping
# load NKI data
data(nkis)
# load gene modules
data(mod1)
# fit a Subtype Clustering Model
scmod1.nkis <- subtype.cluster(module.ESR1=mod1$ESR1, module.ERBB2=mod1$ERBB2,
  module.AURKA=mod1$AURKA, data=data.nkis, annot=annot.nkis, do.mapping=TRUE,
  do.scale=TRUE, plot=TRUE, verbose=TRUE)
str(scmod1.nkis, max.level=1)
table(scmod1.nkis$subtype2)

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

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)

Description

This function computes signature scores from gene expression values following the algorithm used for the Tamoxifen Resistance signature (TAMR13).

Usage

tamr13(data, annot, do.mapping = FALSE, mapping, verbose = FALSE)

Arguments

Value

A list with items:

• score: Continuous signature scores.

• risk: Binary risk classification, 1 being high risk and 0 being low risk (not implemented, the function will return NA values).

References

Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, Gillet C, Ellis P, Ryder K, Reid JF, Daidone MG, Pierotti MA, Berns EMJJ, Jansen MPHM, Foekens JA, Delorenzi M, Bontempi G, Piccart MJ and Sotiriou C (2008) "Predicting prognosis using molecular profiling in estrogen receptor- positive breast cancer treated with tamoxifen", BMC Genomics, 9(1):239

See Also

gene76

Examples

# load TAMR13 signature
data(sig.tamr13)
# load VDX dataset
data(vdxs)
# compute relapse score
tamr13.vdxs <- tamr13(data=data.vdxs, annot=annot.vdxs, do.mapping=FALSE)
summary(tamr13.vdxs$score)

Description

Computation of Tukey's Biweight Robust Mean, a robust average that is unaffected by outliers.

Usage

tbrm(x, C = 9)

Arguments

Details

This is a one step computation that follows the Affy whitepaper below see page 22. This function is called by chron to calculate a robust mean. C determines the point at which outliers are given a weight of 0 and therefore do not contribute to the calculation of the mean. C=9 sets values roughly +/-6 standard deviations to 0. C=6 is also used in tree-ring chronology development. Cook and Kairiukstis (1990) have further details. Retrieved from tbrm.

Value

A numeric mean.

References

Statistical Algorithms Description Document, 2002, Affymetrix. p22. Cook, E. R. and Kairiukstis, L.A. (1990) Methods of Dendrochronology: Applications in the Environmental Sciences. ISBN-13: 978-0792305866. Mosteller, F. and Tukey, J. W. (1977) Data Analysis and Regression: a second course in statistics. Addison-Wesley. ISBN-13: 978-0201048544.

See Also

chron

Examples

tbrm(rnorm(100))

Description

This dataset contains (part of) the gene expression, annotations and clinical data as published in Wang et al. 2005 and Minn et al 2007.

Format

vdxs is a dataset containing three matrices:

• data.vdxs: Matrix containing gene expressions as measured by Affymetrix hgu133a technology (single-channel, oligonucleotides)

• annot.vdxs: Matrix containing annotations of ffymetrix hgu133a microarray platform

• demo.vdxs: Clinical information of the breast cancer patients whose tumors were hybridized

Details

This dataset represent only partially the one published by Wang et al. 2005 and Minn et al 2007. Indeed only part of the patients (150) and gene expressions (966) are contained in data.vdxs.

Source

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2034

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5327

References

Y. Wang and J. G. Klijn and Y. Zhang and A. M. Sieuwerts and M. P. Look and F. Yang and D. Talantov and M. Timmermans and M. E. Meijer-van Gelder and J. Yu and T. Jatkoe and E. M. Berns and D. Atkins and J. A. Foekens (2005) "Gene-Expression Profiles to Predict Distant Metastasis of Lymph-Node-Negative Primary Breast Cancer", Lancet, 365:671–679

Minn, Andy J. and Gupta, Gaorav P. and Padua, David and Bos, Paula and Nguyen, Don X. and Nuyten, Dimitry and Kreike, Bas and Zhang, Yi and Wang, Yixin and Ishwaran, Hemant and Foekens, John A. and van de Vijver, Marc and Massague, Joan (2007) "Lung metastasis genes couple breast tumor size and metastatic spread", Proceedings of the National Academy of Sciences, 104(16):6740–6745

Description

This function allows for computing the weighted mean and weighted variance of a vector of continuous values.

Usage

weighted.meanvar(x, w, na.rm = FALSE)

Arguments

Details

If w is missing then all elements of x are given the same weight, otherwise the weights coerced to numeric by as.numeric. On the contrary of weighted.mean the weights are NOT normalized to sum to one. If the sum of the weights is zero or infinite, NAs will be returned.

Value

A numeric vector of two values that are the weighted mean and weighted variance respectively.

References

http://en.wikipedia.org/wiki/Weighted_variance#Weighted_sample_variance

See Also

stats::weighted.mean

Examples

set.seed(54321)
weighted.meanvar(x=rnorm(100) + 10, w=runif(100))

Description

This function allows for writing a 'csv' file containing gene signatures. Each gene signature is composed of at least four columns: "gene.list" is the name of the signature on the first line and empty fields below, "probes" are the probe names, "EntrezGene.ID" are the EntrezGene IDs and "coefficient" are the coefficients of each probe.

Usage

write.m.file(obj, file, ...)

Arguments

Value

None.

Examples

# load gene modules published by Demsedt et al 2009
data(mod1)
# write these gene modules in a 'csv' file
# Not run: write.m.file(obj=mod1, file="desmedt2009_genemodules.csv")