Package 'a4Classif'

Title: Automated Affymetrix Array Analysis Classification Package
Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages.
Authors: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre]
Maintainer: Laure Cougnaud <[email protected]>
License: GPL-3
Version: 1.53.0
Built: 2024-10-01 04:54:12 UTC
Source: https://github.com/bioc/a4Classif

Help Index

Classify using the Lasso

Description

Classify using the Lasso

Usage

lassoClass(object, groups)

Arguments

object

object containing the expression measurements; currently the only method supported is one for ExpressionSet objects
groups

character string indicating the column containing the class membership

Value

object of class glmnet

Author(s)

Willem Talloen

References

Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, pp. 183, 205 and 212.

See Also

glmnet

Examples

if (require(ALL)){
  data(ALL, package = "ALL")
  ALL <- addGeneInfo(ALL)
  ALL$BTtype <- as.factor(substr(ALL$BT,0,1))

  resultLasso <- lassoClass(object = ALL, groups = "BTtype")
  plot(resultLasso, label = TRUE,
    main = "Lasso coefficients in relation to degree of
  penalization.")
  topTable(resultLasso, n = 15)
}

Classify using Prediction Analysis for MicroArrays

Description

Classify using the Prediction Analysis for MicroArrays (PAM) algorithm as implemented in the pamr package

Usage

pamClass(object, groups, probe2gene = TRUE)

Arguments

object

object containing the expression measurements; currently the only method supported is one for ExpressionSet objects
groups

character string indicating the column containing the class membership
probe2gene

logical; if TRUE Affymetrix probeset IDs are translated into gene symbols; if FALSE no such translation is conducted

Value

object of class pamClass

Author(s)

Willem Talloen

References

Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu (1999). Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99: 6567-6572. Available at www.pnas.org

Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix Microarrays, Chapman \& Hall/CRC, p. 221.

See Also

pamr.train

Examples

if(require(ALL)){
 data(ALL, package = "ALL")
 ALL <- addGeneInfo(ALL)
 ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
 resultPam <- pamClass(object = ALL, groups = "BTtype")
 plot(resultPam)
 topTable(resultPam, n = 5)
 confusionMatrix(resultPam)
}

Classify using Random Forests

Description

Classify using the Random Forest algorithm of Breiman (2001)

Usage

rfClass(object, groups, probe2gene = TRUE)

Arguments

object

object containing the expression measurements; currently the only method supported is one for ExpressionSet objects
groups

character string indicating the column containing the class membership
probe2gene

logical; if TRUE Affymetrix probeset IDs are translated into gene symbols in the output object; if FALSE no such translation is conducted

Value

Object of class 'rfClass'

Note

topTable and plot methods are available for 'rfClass' objects.

Author(s)

Tobias Verbeke and Willem Talloen

References

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

See Also

randomForest

Examples

if(require(ALL)){
 data(ALL, package = "ALL")
 ALL <- addGeneInfo(ALL)
 ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
 # select only a subset of the data for computation time reason
 ALLSubset <- ALL[sample.int(n = nrow(ALL), size = 100, replace = TRUE), ]
 resultRf <- rfClass(object = ALLSubset, groups = "BTtype")
 plot(resultRf)
 topTable(resultRf, n = 15)
}

Receiver operating curve

Description

A ROC curve plots the fraction of true positives (TPR = true positive rate) versus the fraction of false positives (FPR = false positive rate) for a binary classifier when the discrimination threshold is varied. Equivalently, one can also plot sensitivity versus (1 - specificity).

Usage

ROCcurve(
  object,
  groups,
  probesetId = NULL,
  geneSymbol = NULL,
  main = NULL,
  probe2gene = TRUE,
  ...
)

Arguments

object

ExpressionSet object for the experiment
groups

String containing the name of the grouping variable. This should be a the name of a column in the pData of the expressionSet object.
probesetId

The probeset ID. These should be stored in the featureNames of the expressionSet object.
geneSymbol

The gene symbol. These should be stored in the column `Gene Symbol` in the featureData of the expressionSet object.
main

Main title on top of the graph
probe2gene

Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot)
...

Possibility to add extra plot options. See par

Value

a plot is drawn in the current device. prediction object is returned invisibly.

Author(s)

Willem Talloen

References

Some explanation about ROC can be found on http://en.wikipedia.org/wiki/ROC_curve and http://www.anaesthetist.com/mnm/stats/roc/Findex.htm. The latter has at the bottom a nice interactive tool to scroll the cut-off and to see how it affects the FP/TP table and the ROC curve.

Examples

# simulated data set
esSim <- simulateData()
ROCcurve(probesetId = 'Gene.1', object = esSim, groups = 'type', addLegend = FALSE)

Top table for pamClass object

Description

Top table for pamClass object

Usage

## S4 method for signature 'pamClass'
topTable(fit, n)

Arguments

fit

object for which to obtain a top table, generally a fit object for a given model class
n

number of features (variables) to list in the top table, ranked by importance

Value

topTablePam object

Top table for rfClass object

Description

Top table for rfClass object

Usage

## S4 method for signature 'rfClass'
topTable(fit, n)

Arguments

fit

object for which to obtain a top table, generally a fit object for a given model class
n

number of features (variables) to list in the top table, ranked by importance

Value

topTableRfClass object