biosign: Builds the molecular signature.

Description

Main function of the 'biosigner' package. For each of the available classifiers (PLS-DA, Random Forest, and SVM), the significant features are selected and the corresponding models are built.

Usage

## S3 method for class 'data.frame':
biosign(x, ...)

## S3 method for class 'matrix':
biosign(x,
y,
methodVc = c("all", "plsda", "randomforest", "svm")[1],
bootI = 50,
pvalN = 0.05,

permI = 1,
fixRankL = FALSE,

printL = TRUE,
plotL = TRUE,

.sinkC = NULL,
...)

Arguments

Numerical data frame or matrix (observations x variables); NAs are allowed for PLS-DA but for SVM, samples with NA will be removed

Factor: Two-level factor corresponding to the class labels

methodVc

Character vector: Either one or all of the following classifiers: Partial Least Squares Discriminant Analysis ('plsda'), or Random Forest ('randomforest'), or Support Vector Machine ('svm')

bootI

Integer: Number of bootstaps for resampling

pvalN

Numeric: To speed up the selection, only variables which significantly improve the model up to two times this threshold (to take into account potential fluctuations) are computed

permI

Integer: Random permutation are used to assess the significance of each new variable included into the model (forward selection)

fixRankL

Logical: Should the initial ranking be computed with the full model only, or as the median of the ranks from the models built on the sampled dataset?

printL

Logical: Should informations regarding the data set and the model be printed? [default = TRUE]

plotL

Logical: Should the 'summary' plot be displayed? [default = TRUE]

.sinkC

Character: Name of the file for R output diversion [default = NULL: no diversion]; Diversion of messages is required for the integration into Galaxy

...

Currently not used.

Value

An S4 object of class 'biosign' containing the following slots: 1) 'methodVc' character vector: selected classifier(s) ('plsda', 'randomforest', and/or 'svm'), 2) 'accuracyMN' numeric matrix: balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures (predictions are obtained by using the resampling scheme selected with the 'bootI' and 'crossvalI' arguments), 3) 'tierMC' character matrix: contains the tier ('S', 'A', 'B', 'C', 'D', or 'E') of each feature for each classifier (features with tier 'S' have been found significant in all backward selections; features with tier 'A' have been found significant in all but the last selection, and so on), 4) modelLs list: selected classifier(s) trained on the subset restricted to the 'S' features, 5) signatureLs list: 'S' signatures for each classifier; and 6) 'AS' list: 'AS' signatures and corresponding trained classifiers, in addition to the dataset restricted to tiers 'S' and 'A' ('xMN') and the labels ('yFc')

Examples

Run this code

## loading the diaplasma dataset

data(diaplasma)
attach(diaplasma)

## restricting to a smaller dataset for this example

featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500
dataMatrix <- dataMatrix[, featureSelVl]
variableMetadata <- variableMetadata[featureSelVl, ]

## signature selection for all 3 classifiers
## a bootI = 5 number of bootstraps is used for this example
## we recommend to keep the default bootI = 50 value for your analyzes

set.seed(123)
diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5)

detach(diaplasma)

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Description

Usage

Arguments

Value

See Also

Examples