PSCBS (version 0.67.0)

callGainsAndLosses.CBS: Calls gains and losses

Description

Calls gains and losses.

Usage

# S3 method for CBS
callGainsAndLosses(fit, adjust=1, method=c("ucsf-mad", "ucsf-dmad"), ..., verbose=FALSE)

Value

Returns a CBS object where logical columns 'lossCall' and 'gainCall' have been appended to the segmentation table.

Arguments

adjust

A positive scale factor adjusting the sensitivity of the caller, where a value less (greater) than 1.0 makes the caller less (more) sensitive.

method

A character string specifying the calling algorithm to use.

...

Additional/optional arguments used to override the default parameters used by the caller.

The UCSF caller

If method == "ucsf-mad", then segments are called using [1], i.e. a segment is called gained or lost if its segment level is at least two standard deviations away from the median segment level on Chr1-22, where standard deviation is estimated using MAD. Then same is done for method == "ucsf-dmad" with the difference that the standard deviation is estimated using a robust first order variance estimator.

Author

Henrik Bengtsson

References

[1] Fridlyand et al. Breast tumor copy number aberration phenotypes and genomic instability, BMC Cancer, 2006.

See Also

*callAmplifications(). *callOutliers(). For more information see CBS.

Examples

 
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Simulating copy-number data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
set.seed(0xBEEF)

# Number of loci
J <- 1000

mu <- double(J)
mu[200:300] <- mu[200:300] + 1
mu[350:400] <- NA # centromere
mu[650:800] <- mu[650:800] - 1
eps <- rnorm(J, sd=1/2)
y <- mu + eps
x <- sort(runif(length(y), max=length(y))) * 1e5
w <- runif(J)
w[650:800] <- 0.001


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Segmentation
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
fit <- segmentByCBS(y, x=x)
print(fit)
plotTracks(fit)


 
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# CALLS
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Call gains and losses by segments
fitC <- callGainsAndLosses(fit)

# Call amplifications by segments
fitC <- callAmplifications(fitC)

# Call outliers by loci
fitC <- callOutliers(fitC)