summary.cghFLasso: Summarizing gain/loss calls by cghFLasso

In cghFLasso: Detecting hot spot on CGH array data with fused lasso regression.

Description

'summary' method for class cghFLasso.

Usage

         summary.cghFLasso(object, index,...)
         print.summary.cghFLasso(x,...)

Arguments

object	an object of class cghFLasso (returned by function cghFLasso).
index	numeric vector specifying which arrays to plot.
x	an object of class summary.cghFLasso, usually, a result of a call to summary.cghFLasso.
...	further arguments passed to or from other methods.

Details

summary.cghFLasso summarize the gain/loss calls for a group of CGH arrays. It reports the consensus counts of alterations as well as the corresponding FDRs for each gene/clones. It also returns the sample percentage of amplificaiton and deletion at each gene/cloens.

print.summary.cghFLasso outputs a matrix summarizing the gain/loss calls across all the samples for each chromosome. It returns the maximum sample percentage of amplificaiton and deletion on each chromosome respectively. It also reports the genome percentage of alteration for each chromosome (proportion of genes having consensus FDR smaller than 0.05 on the chromosome).

Value

ans<-list(ConsensusCount=ConsensusCount, CC.FDR=CC.FDR, Amp.ConCount=Amp.CC, Del.ConCount=Del.CC, chrom.summary=chrom.summary, sample.summary=sample.summary, Esti.copy=Esti.copy, chromosome=chromosome, nucposi=nucposi)

ConsensusCount	numeric vector with the same length as each CGH array. It reports the number of samples showing copy number alteration at each gene/clones
CC.FDR	numeric vector of the same length of ConsensusCount. It is the estiamted probability of observing the same or higher consensus count by random chance.
Amp.ConCount	numeric vector of the same length of ConsensusCount. It reports the number of samples showing amplificaiton at each gene/clones.
Del.ConCount	numeric vector of the same length of ConsensusCount. It reports the number of samples showing deletion at each gene/clones.
chrom.summary	matrix with four columns. The first column is the chromosome number. The second and third columns are the maximum sample percentages of amplificaitons and deletions on each chromosome respectively. The forth column represents the genome percentage of alterations on each chromosome.
sample.summary	numeric vector with the same lenght of sample numbers. It reports the overall alteration percentage of each sample.
Esti.copy	numeric matrix showing estiamted copy numbers of all genes/clones of all samples.
chromosome	numeric vector with the same length as each CGH array. It's the chromosome number of each gene/clone.
nucposi	numeric vector with the same length as each CGH array. It's the nucletide position of each gene/clone.

Author(s)

N. A. Johnson, R. Tibshirani and P. Wang

References

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu and K. Knight (2004) ‘Sparsity and smoothness via the fused lasso’, J. Royal. Statist. Soc. B. (In press), available at http://www-stat.stanford.edu/~tibs/research.html.

P. Wang, Y. Kim, J. Pollack, B. Narasimhan and R. Tibshirani (2005) ‘A method for calling gains and losses in array CGH data’, Biostatistics 2005, 6: 45-58, available at http://www-stat.stanford.edu/~wp57/CGH-Miner/

R. Tibshirani and P. Wang (2007) ‘Spatial smoothing and hot spot detection using the Fused Lasso’, Biostatistics (In press), available at http://www-stat.stanford.edu/~tibs/research.html.

J. Friedman, T. Hastie. R. Tibshirani (2007) ‘Pathwise coordinate optimization and the fused lasso’.

Examples

library(cghFLasso)
data(CGH)

#############
### Example 1: Process one chromosome vector without using normal references.

CGH.FL.obj1<-cghFLasso(CGH$GBM.y)
plot(CGH.FL.obj1, index=1, type="Lines")

#############
### Example 2: Process a group of CGH arrays and use normal reference arrays.

Normal.FL<-cghFLasso.ref(CGH$NormalArray,  chromosome=CGH$chromosome)
Disease.FL<-cghFLasso(CGH$DiseaseArray, chromosome=CGH$chromosome, nucleotide.position=CGH$nucposition, FL.norm=Normal.FL, FDR=0.01)

###  Plot for the first arrays
i<-1
plot(Disease.FL, index=i, type="Single")
title(main=paste("Plot for the ", i ,"th BAC array", sep=""))

### Consensus plot
plot(Disease.FL, index=1:4, type="Consensus")
title(main="Consensus Plot for 4 BAC arrays")

### Plot all arrays
plot(Disease.FL, index=1:4, type="All")
title(main="Plot for all 4 arrays")

### Report and output
report<-summary(Disease.FL, index=1:4)
print(report)
output.cghFLasso(report, file="CGH.FL.output.txt")

cghFLasso documentation built on May 29, 2017, 7:18 p.m.