CombinedWeights-class {plasma} | R Documentation |
Class "CombinedWeights"
Description
The CombinedWeights
object class merges the weight matrices for
all data sets in a plasma object.
Usage
combineAllWeights(pl)
## S4 method for signature 'CombinedWeights'
summary(object, ...)
## S4 method for signature 'CombinedWeights'
image(x, ...)
stdize(object, type = c("standard", "robust"))
interpret(object, component, alpha = 0.05)
Arguments
pl |
An object of the |
object |
An object of the |
x |
An object of the |
type |
A single character string indicating how to standardize the object. Legal value are "standard" or "robust". |
component |
A single chaaracter string; which componen should be interpreted. |
alpha |
A single numerical value between 0 and 1; what signfiicance value should be used to select important features. |
... |
Ignored; potentially, extra arguments to the summary or image methods. |
Value
The combineAllWeights
function returns a newly constructed object of the
CombinedWeights
class. The summary method returna list
containing four matrices. Each matrix has one row for each omics data
set and one column for each model component. Each amtric contains
different summary statistics, including the Mean, SD, Median, and MAD.
Objects from the Class
Objects are defined using the combineAllWeights
functions.
Simply supply an object of class plasma
.
Slots
combined
:a matrix of the original variables in dataset
N
as rows and the PLS componentsM
as columns.featureSize
:a numeric (usually integer) vector that stores the number of features in each omics data set.
dataSource
:a factor indicating which omics data set each feature came from.
Methods
summary
:outputs summary statistics for the contributions of dataset
N
to components from all datasets in the case ofgetAllWeights
or datasetM
in the case ofgetCompositeWeights
.
Author(s)
Kevin R. Coombes krc@silicovore.com, Kyoko Yamaguchi kyoko.yamaguchi@osumc.edu
Examples
fls <- try(loadESCAdata())
if (inherits(fls, "try-error")) {
stop("Unable to load data from remote server.")
}
# restrict data set size
MO <- with(plasmaEnv, prepareMultiOmics(
assemble[c("ClinicalBin", "ClinicalCont", "RPPA")], Outcome))
splitVec <- with(plasmaEnv, rbinom(nrow(Outcome), 1, 0.6))
trainD <- MO[, splitVec == 1]
testD <- MO[, splitVec == 0]
firstPass <- fitCoxModels(trainD, "Days", "vital_status", "dead")
pl <- plasma(object = trainD, multi = firstPass)
getCompositeWeights(object = pl, N = "ClinicalBin", M = "RPPA")
cbin <- getAllWeights(object = pl, N = "ClinicalBin")
summary(cbin)
image(cbin)
heat(cbin, cexCol = 0.5)
cbin01 <- pickSignificant(object = cbin, alpha = 0.01)
image(cbin01)
heat(cbin01, cexCol = 0.5)
getTop(object = cbin01, N = 3)