| GenomicReports {beadarraySNP} | R Documentation |
Create reports for all samples in a dataset.
reportChromosomesSmoothCopyNumber(snpdata, grouping, normalizedTo=2,
smooth.lambda=2, ridge.kappa=0, plotLOH=c("none", "marker", "line", "NorTum"),
sample.colors = NULL, ideo.bleach=0.25, ...)
reportSamplesSmoothCopyNumber(snpdata, grouping, normalizedTo=2,
smooth.lambda=2, ridge.kappa=0, plotLOH=c("none", "marker", "line", "NorTum"),
sample.colors=NULL, ...)
reportGenomeGainLossLOH(snpdata, grouping, plotSampleNames=FALSE, sizeSampleNames=4,
distance.min, upcolor="red", downcolor="blue", lohcolor="grey", hetcolor="lightgrey",
lohwidth=1, segment=101, orientation=c("V","H"), ...)
reportChromosomeGainLossLOH(snpdata, grouping, plotSampleNames=FALSE, distance.min,
upcolor="red", downcolor="blue", lohcolor="grey", hetcolor="lightgrey", proportion=0.2,
plotLOH=TRUE, segment=101, ...)
reportGenomeIntensityPlot(snpdata, normalizedTo=NULL, subsample=NULL, smoothing=c("mean", "quant"),
dot.col="black", smooth.col="red", ...)
snpdata |
SnpSetIllumina object. |
grouping |
factor, elements with same value are plotted together. Defaults to groups of 4 in order of the samples in the object. |
normalizedTo |
numeric, a horizontal line is drawn at this position. |
smooth.lambda |
smoothing parameter for quantsmooth. |
ridge.kappa |
smoothing parameter for quantsmooth. |
plotLOH |
indicate regions or probes with LOH, see details. |
sample.colors |
vector of color. |
plotSampleNames |
logical. |
sizeSampleNames |
numeric, margin size for sample names. |
distance.min |
numerical. |
upcolor |
color. |
downcolor |
color. |
lohcolor |
color. |
hetcolor |
color. |
lohwidth |
|
segment |
integer. |
orientation |
["V","H"], vertical or horizontal orientation of plot. |
proportion |
|
subsample |
|
smoothing |
Type of smoothing per chromosome. |
dot.col |
color. |
smooth.col |
color. |
ideo.bleach |
numeric [0,1] |
... |
arguments are forwarded to plot or getChangedRegions. |
The first function creates plots for each group and each chromosome in the
dataset. The second function creates full genome plot for each group in the
dataset. Beware that a lot of plots can be created, and usually you should
prepare for that, by redirecting the plots to pdf or functions that
create picture files like jpg, png, bmp.
These functions are executed for their side effects
Jan Oosting
quantsmooth,prepareGenomeplot,
pdfChromosomesSmoothCopyNumber, pdfSamplesSmoothCopyNumber
data(chr17.260) chr17nrm <- standardNormalization(chr17.260) par(mfrow = c(4,2), mar = c(2,4,2,1)) reportChromosomesSmoothCopyNumber(chr17nrm, grouping=pData(chr17.260)$Group,smooth.lambda = 4)