Iterative Bayesian Model Averaging: training and prediction

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Description

Classification and variable selection on microarray data. This is a multivariate technique to select a small number of relevant variables (typically genes) to classify microarray samples. This function performs the training, and prediction steps. The data is assumed to consist of two classes. Logistic regression is used for classification.

Usage

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iterateBMAglm.train.predict (train.expr.set, test.expr.set, train.class, p=100, nbest=10, maxNvar=30, maxIter=20000, thresProbne0=1)

Arguments

train.expr.set

an ExpressionSet object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. This training data is used to select relevant genes (variables) for classification.

test.expr.set

an ExpressionSet object. We assume the rows in the expression data represent variables (genes), while the columns represent samples or experiments. The variables selected using the training data is used to classify samples on this test data.

train.class

class vector for the observations (samples or experiments) in the training data. Class numbers are assumed to start from 0, and the length of this class vector should be equal to the number of rows in train.dat. Since we assume 2-class data, we expect the class vector consists of zero's and one's.

p

a number indicating the maximum number of top univariate genes used in the iterative BMA algorithm. This number is assumed to be less than the total number of genes in the training data. A larger p usually requires longer computational time as more iterations of the BMA algorithm are potentially applied. The default is 100.

nbest

a number specifying the number of models of each size returned to bic.glm in the BMA package. The default is 10.

maxNvar

a number indicating the maximum number of variables used in each iteration of bic.glm from the BMA package. The default is 30.

maxIter

a number indicating the maximum of iterations of bic.glm. The default is 20000.

thresProbne0

a number specifying the threshold for the posterior probability that each variable (gene) is non-zero (in percent). Variables (genes) with such posterior probability less than this threshold are dropped in the iterative application of bic.glm. The default is 1 percent.

Details

This function consists of the training phase and the prediction phase. The training phase consists of first ordering all the variables (genes) by a univariate measure called between-groups to within-groups sums-of-squares (BSS/WSS) ratio, and then iteratively applying the bic.glm algorithm from the BMA package. The prediction phase uses the variables (genes) selected in the training phase to classify the samples in the test set.

Value

A vector consisting of the predicted probability that each test sample belongs to class 1 is returned.

Note

The BMA and Biobase packages are required.

References

Raftery, A.E. (1995). Bayesian model selection in social research (with Discussion). Sociological Methodology 1995 (Peter V. Marsden, ed.), pp. 111-196, Cambridge, Mass.: Blackwells.

Yeung, K.Y., Bumgarner, R.E. and Raftery, A.E. (2005) Bayesian Model Averaging: Development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21: 2394-2402.

See Also

iterateBMAglm.train, iterateBMAglm.train.predict.test, brier.score

Examples

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library (Biobase)
library (BMA)
library (iterativeBMA)
data(trainData)
data(trainClass)
data (testData)

ret.vec <- iterateBMAglm.train.predict (train.expr.set=trainData, test.expr.set=testData, trainClass, p=100)

## compute the Brier Score
data (testClass)
brier.score (ret.vec, testClass)