MAplotPara(object,
log=TRUE,
type=c("both","pm","mm"),
ref=NULL,
which=NULL,
ref.title="vs mean ref.Array",
subset=NULL,
span=1/4,
show.statistics=TRUE,
family.loess ="gaussian",
pchs=".",
plot =TRUE,
cutoff =0.5,# add parameter to generic function ma.plot
level=1,
cluster, verbose = getOption("verbose"),
... )
character
vector with the names of CEL files
OR a (partitioned) list of character
vectors with CEL file names."guassian"
or "symmetric"
as in loess."."
equivalently to pchs = 46
from the function points.affyParaInternalEnv$cl
will be used.TRUE
it writes out some messages. default: getOption("verbose")values_MAP
, loess_y
, quality_MAP
, results_MAP
.sampleNames
, S
, osc_Loess
, sigma
, var_sigma
).osc_Loess
from values_MAP
quality_MAP
as matrix, which contains only the Arrays that are considered as "bad" quality and in which levels are they classified. Possible values are 0 if the Array is not at this levels and 1 if it is classified as "bad" sample at this level.MAplotPara
is the parallelized function for MA plots of probe intensities.
It is a function to check and control the Data quality of the samples using the MA plot method.
For serial function an more details see boxplot. This function is optimized for huge numbers of microarray data.
For using this function a computer cluster using the SNOW package has to be started.
Starting the cluster with the command makeCluster
generates an cluster object in the affyPara environment (.affyParaInternalEnv) and
no cluster object in the global environment. The cluster object in the affyPara environment will be used as default cluster object,
therefore no more cluster object handling is required.
The makeXXXcluster
functions from the package SNOW can be used to create an cluster object in the global environment and
to use it for the preprocessing functions.
library(affyPara)
if (require(affydata)) {
data(Dilution)
makeCluster(3)
##MA of Dilution data (affybatch)
ma1 <- MAplotPara(Dilution)
## MAplot to a pdf file
pdf(file="maplot.pdf", title="AffyBatch MAplot")
ma2 <- MAplotPara(Dilution)
dev.off()
stopCluster()
}
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