Description Usage Arguments Details Value Author(s) References See Also Examples
Calls aberrations for array CGH data using a six state mixture model.
1 |
inputSegmented |
An object of class |
prior |
Options are |
nclass |
The number of levels to be used for calling. Either |
organism |
Either |
cellularity |
A vector of cellularities ranging from 0 to 1 to define the contamination of your sample with healthy cells (1 = no contamination). See details for more information. |
robustsig |
Options are |
nsegfit |
Maximum number of segments used for fitting the mixture model. Posterior probabilities are computed for all segments |
maxnumseg |
Maximum number of segments per profile used for fitting the model |
minlsforfit |
Minimum length of the segment (in Mb) to be used for fitting the model |
build |
Build of Humane Genome. Either |
ncpus |
Number of cpus used for parallel calling. Has a large effect on computing time.
|
Please read the article and the supplementary information for detailed information on the algorithm.
The parameter prior
states how the data is used to determine the prior probabilities. When set to all
, the probabilities are determined using the entire genome of each sample.
When set to not all
probabilites are determined per chromosome for each sample when organism
is set to other
or per chromosome arm when organism
is human
.
The chromosome arm information is taken from the March 2006 version of the UCSC database. When prior
is set to auto
, the way probabilities are determined depends on the sample size. The entire genome is used when the sample size is smaller than 20, otherwise chromosome (arm) information is used.
Please note that CGHcall uses information from all input data to determine the aberration probabilities.
When for example triploid or tetraploid tumors are observed, we advise to run CGHcall separately on
those (groups of) samples. Note that robustsig = yes
enforces the sd corresponding to
the normal segments to be at least half times the pooled gain/loss sd. Use of nsegfit
significantly lower computing
time with respect to previous CGHcall versions without much accuracy loss. Moreover, maxnumseg
decreases the
impact on the results of profiles with inferior segmentation results. Finally, minlsforfit
decreases the impact
of very small aberations (potentially CNVs rather than CNAs) on the fit of the model. Note that always a result for all
segments is produced. IN MOST CASES, CGHcall SHOULD BE FOLLOWED BY FUNCTION ExpandCGHcall.
This function return a list with six components:
posteriorfin2 |
Matrix containing call probabilities for each segment. First column denotes profile number, followed by k columns with aberration probabilities for each sample, where k is the number of levels used for calling ( |
nclone |
Number of clone or probes |
nc |
Number of samples |
nclass |
Number of classes used |
regionsprof |
Matrix containing information about the segments, 4 colums: profile, start probe, end probe, segmented value |
params |
Vector containing the parameter values of the mixture model |
Sjoerd Vosse, Mark van de Wiel, Ilari Scheinin
Mark A. van de Wiel, Kyung In Kim, Sjoerd J. Vosse, Wessel N. van Wieringen, Saskia M. Wilting and Bauke Ylstra. CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics, 23, 892-894.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(Wilting)
## Convert to \code{\link{cghRaw}} object
cgh <- make_cghRaw(Wilting)
print(cgh)
## First preprocess the data
raw.data <- preprocess(cgh)
## Simple global median normalization for samples with 75% tumor cells
normalized.data <- normalize(raw.data)
## Segmentation with slightly relaxed significance level to accept change-points.
## Note that segmentation can take a long time.
## Not run: segmented.data <- segmentData(normalized.data, alpha=0.02)
## Not run: postsegnormalized.data <- postsegnormalize(segmented.data)
## Call aberrations
perc.tumor <- rep(0.75, 3)
## Not run: result <- CGHcall(postsegnormalized.data,cellularity=perc.tumor)
## Expand to CGHcall object
## Not run: result <- ExpandCGHcall(result,postsegnormalized.data)
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