plrs
models for a series of arrays. Model selection and testing procedures may be applied.
plrs.series(expr, cghseg, cghcall=NULL,
probloss = NULL, probnorm = NULL, probgain = NULL, probamp = NULL,
control.model = list(continuous = FALSE, constr = TRUE, constr.slopes = 2, constr.intercepts = TRUE, min.obs = 3, discard.obs = TRUE),
control.select = list(crit = ifelse(control.model$constr, "osaic","aic")),
control.test = list(testing = TRUE, cb = FALSE, alpha = 0.05),
control.output = list(save.models = FALSE, save.plots = FALSE, plot.lin = FALSE, type = "jpeg"))
ExpressionSet
object.NULL
.NULL
.NULL
.NULL
.ExpressionSet
object and aCGH data
as cghSeg
or cghCall
objects. A cghCall
object
contain all data from the calling step, thus arguments probloss
, probnorm
, probnorm
and probamp
can be omitted. An object of class cghSeg
does not contain such data so only simple linear models
will be fitted.control.model
allows the user to specify the type of model that has to be fitted.
This must be a list
with one or more of the following components:
constr
, constr.slopes
, constr.intercepts
, min.obs
and discard.obs
.
See functions plrs
and modify.conf
for more details.
control.select
allows the user to specify whether model selection should be done and how.
This must be a list
with a component named crit
. See function plrs.select
for more details.
If control.select = NULL
then no model selection is done.
control.output
allows the user to plot and save each plrs
model. This must be a list with components:
save.models
, a logical. This will create within the work directory a new directory named "plrsSeriesObjects" that will contain all objects.
save.plots
, a logical. This will create within the work directory a new directory named "plrsSeriesPlots" that will contains all saved plots.
plot.lin
, a logical. Whether the simple linear model should aslo be plotted.
type
, a character. Format of file. To pass through function savePlot
.
# Simulate data
ngenes <- 10
narray <- 48
rna <- dnaseg <- dnacal <- matrix(NA, ngenes, narray)
idx <- sample(1:4, ngenes, replace=TRUE, prob=rep(1/4,4))
for(i in 1:ngenes){
Sim <- plrs.sim(n=narray, states=idx[i], sigma=0.5)
rna[i,] <- Sim$expr
dnaseg[i,] <- Sim$seg
dnacal[i,] <- Sim$cal
}
# Screening procedure with linear model
series <- plrs.series(expr = rna, cghseg = dnaseg, cghcall = NULL, control.select = NULL)
# Screening procedure with full plrs model
series <- plrs.series(expr = rna, cghseg = dnaseg, cghcall = dnacal, control.select = NULL)
# Model selection
series <- plrs.series(expr = rna, cghseg = dnaseg, cghcall = dnacal)
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