EMnormalize {rCGH} | R Documentation |
This function analyses the Log2Ratios as a mixture of several gaussian
populations, using an Expectation-Maximization algorithm (EM).
The peakThresh
argument specifies what proportion of the main density
peak is allowed for choosing a neutral 2-copies population. The mean of the
chosen population is used for centralizing the profile.
See Mclust
.
## S4 method for signature 'rCGH' EMnormalize(object, G = 2:6, priorScale = 5, peakThresh = 0.5, mergeVal = 0.1, Title = NA, verbose = TRUE)
object |
: An object of class |
G |
: numeric. The number of groups to test during the gaussian mixture estimation. Default is from 2 to 6. |
priorScale |
: numeric. A scale value passed to |
peakThresh |
: numeric. The proportion of the highest peak to consider as a peak selection threshold. Default is 0.5. |
mergeVal |
: numeric. Populations with means closer than |
Title |
: character string. A title for the density plot. If |
verbose |
: logical. When |
Depending on peakThresh
, the mean of the highest density, or a lower
value, is chosen for centering the genomic profile. To do so, L2R are modeled
for each segment s_i, with respect to n_i (the number of probes included in
segment i), mu_i and sd_i. The mixture of L2Rs is then analysed as a mixture of
gaussian populations.
When a peakThresh
value is specified, heights of density peaks are
compared: the lowest peak mean among the peaks respecting the criteria:
peakHeight > max(peaks)*peakThresh
, is chosen for centralizing the data.
See References
An object of same class as the input.
Frederic Commo
filePath <- system.file("extdata", "Affy_cytoScan.cyhd.CN5.CNCHP.txt.bz2", package = "rCGH") cgh <- readAffyCytoScan(filePath, sampleName = "AffyScHD") cgh <- adjustSignal(cgh, nCores=1) cgh <- segmentCGH(cgh, nCores=1) cgh <- EMnormalize(cgh) getParam(cgh)