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

Description Usage Arguments Value Author(s) References See Also Examples

Description

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

Usage

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

Arguments

sbt.model

Subtype Clustering Model as returned by intrinsic.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

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.

verbose

TRUE to print informative messages, FALSE otherwise.

Value

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.

Author(s)

Benjamin Haibe-Kains

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

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## 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)

genefu documentation built on Nov. 1, 2018, 2:25 a.m.