06-CombinedWeights-class: Class '"CombinedWeights"'

In plasma: Partial LeAst Squares for Multiomic Analysis

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 plasma class.
object	An object of the CombinedWeightss class.
x	An object of the CombinedWeightss class.
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 components M 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 of getAllWeights or dataset M in the case of getCompositeWeights.

Author(s)

Kevin R. Coombes [e-mail], Kyoko Yamaguchi [e-mail]

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)

plasma documentation built on May 9, 2025, 3:01 a.m.