RAP: Rank Aggregation-based Data Fusion for Gene Prioritization
In cma2015/RAP: Rank aggregation-based data fusion for gene prioritization

Description Usage Arguments Value Author(s) Examples

View source: R/RAP.R

This function prioritize genes using sequence-based and network-based model

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  RAP(netPredResult, positives, negatives = NULL, featureSel = TRUE,
      featureMat = NULL, ProteinSeq,  PPIMat, GenomeGeneID, 
      ntree = 500)

`netPredResult`	Full path of gene prioritization results from the network-based gene prioritization algorithms (e.g., AraNet v2).
`featureMat`	A numeric matrix of features where rows represent genes, cols represent features.
`positives`	A character vector of positive samples.
`negatives`	A character vector of negative samples.
`featureSel`	A logical value, where TRUE selecting significant features between positive and negative samples.
`ProteinSeq`	A list of protein sequence, the parameter are not required unless patameter featureMat is NULL.
`PPIMat`	A matrix of PPI, which contains 3 coloums represent protein1, protein2, score respectively, the patameter is not required unless paramter negatives are NULL.
`GenomeGeneID`	A vector of genome ID, the parameter, the parameter is not required unless parameter negatives is NULL.
`ntree`	Number of trees to grow when using random forest, the default is 500.

A matrix with the rank of genes and descriptive information

Jingjing Zhai, Chuang Ma

  ## Not run: 
    positives <- c("AT1G01060", "AT1G09530", "AT1G09570", "AT1G12610")
    res <- RAP(netPredResult = "/home/malab/AraNetPred.txt", 
               positives = positives, negatives = negatives, 
               featureSel = TRUE, featureMat = featureMat)
    ## featureMat can be calculated by function FeatureExtract
    ## negatives can be calculated by function selectNegSamples
    ## The sample results of AraNet v2 (AraNetPred.txt) can be
       downloaded from http://bioinfo.nwafu.edu.cn/software
  
## End(Not run)