iterativeBMA (version 1.30.0)

singleGeneCoxph: Univariate Cox Proportional Hazards Model for selecting top log-ranked predicitve variables

Description

This is a univariate technique to rank variables by their predictive relevance for use in survival analysis on microarray data. The log likelihood is computed for each indiviual variable, where a larger log likelihood value indicates a higher rank.

Usage

singleGeneCoxph(trainData, survData, censoredData)

Arguments

trainData

Data matrix where columns are variables and rows are observations. In the case of gene expression data, the columns (variables) represent genes, while the rows (observations) represent patient samples.

survData

Vector of survival times for the patient samples. Survival times are assumed to be presented in uniform format (e.g., months or days), and the length of this vector should be equal to the number of rows in trainData.

censoredData

Vector of censor data for the patient samples. In general, 0 = censored and 1 = uncensored. The length of this vector should equal the number of rows in trainData and the number of elements in survData.

Value

• This function returns a sorted three-column matrix of the training data variables. The first column gives the variable names with the top log-ranked variable appearing first. The second column gives the original indexes of the variables, and the third column gives the rank of the variables from 1 through ncol(trainData). The matrix is also written to file in the working R directory under the filename 'sorted_loglik.txt'.

Details

This function is called by iterateBMAsurv.train.predict.assess.

References

Annest, A., Yeung, K.Y., Bumgarner, R.E., and Raftery, A.E. (2008). Iterative Bayesian Model Averaging for Survival Analysis. Manuscript in Progress.

Cox, D. (1972). Regression Models and Life Tables. Journal of the Royal Statistical Society Series B 34: 187-220.

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.

Volinsky, C., Madigan, D., Raftery, A., and Kronmal, R. (1997) Bayesian Model Averaging in Proprtional Hazard Models: Assessing the Risk of a Stroke. Applied Statistics 46: 433-448.

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

iterateBMAsurv.train.predict.assess, printTopGenes, trainData, trainSurv, trainCens

Examples

library(BMA)
library(iterativeBMAsurv)
data(trainData)
data(trainSurv)
data(trainCens)

sorted.genes <- singleGeneCoxph(trainData, trainSurv, trainCens)

## Write top 100 genes to file
sorted.top.genes <- printTopGenes(retMatrix=sorted.genes, 100, trainData)

## The file, 'sorted_topCoxphGenes_100', is now in the working R directory.