EMnormalize: Genomic Profile Centralization
In fredcommo/rCGH: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data

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

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.

1
2
3

## 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 `"rCGH"`
`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 `priorControl`. Default is 5.
`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 `mergeVal` will be pooled together, default is 0.1. Set `mergeVal` to zero to not pool closed sub-populations.
`Title`	: character string. A title for the density plot. If `NA` (default), the sample name (when exists in object info) will be used.
`verbose`	: logical. When `TRUE` (default), progress is printed.

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

Commo et al. Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology. Ann Oncol. 2014

plotDensity, mclust

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)