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)