README.md

Package DRWPClassGM implements the DRW-GM method. DRW-GM is a pathway-based classification method which performs classifier construction and precise disease status prediction by joint analysis of genomic and metabolomic data and pathway topology. How to use DRWPClassGM Essential dependence platform To run DRWPClassGM, you should make sure that your computer has installed Weka. The Weka downloaded website (in our analysis we installed weka-3-7-12jre.exe): http://www.cs.waikato.ac.nz/~ml/weka/downloading.html

The available resource of DRWPClass can be downloaded as follows: Package source: DRWPClassGM_1.0.tar.gz

Windows binary: DRWPClassGM_1.0.zip

Reference manual: DRWPClassGM-manual.pdf

http://222.170.78.208/DRW-GM/

Dependent packages: Depends: R (>= 3.1.1), igraph, Matrix, RWeka, samr

#####Example code for predicting disease class

library(DRWPClassGM)

load example data

data(GProf8511) data(GProf3325) data(MProf) data(pathSet) data(dGMGraph)

train a classifier

fit <- fit.DRWPClassGM(xG=GProf8511$mRNA_matrix, yG.class1=GProf8511$normal, yG.class2=GProf8511$PCA, xM=MProf$Meta_matrix, yM.class1=MProf$normal, yM.class2=MProf$PCA, DEBUG=TRUE, pathSet=pathSet, globalGraph=dGMGraph, testStatistic="t-test", classifier = "Logistic")

prediction

predict.DRWPClassGM(object=fit, newx=GProf3325$mRNA_matrix[,c(GProf3325$normal,GProf3325$PCA)], type = "class")

evaluate classification performance

evaluate.DRWPClassGM(object=fit, newx=GProf3325$mRNA_matrix, newy.class1=GProf3325$normal, newy.class2=GProf3325$PCA)



cuihaibo1/123 documentation built on May 12, 2017, 2:20 p.m.