AggregateMC {GeneSelector} | R Documentation

## Aggregation of repeated rankings using a Markov chain approach

### Description

All obtained rankings are aggregated on the basis of Markov chain model, in which each gene constitutes an element of the state space. For details, see DeConde et al. (2006).

### Usage

      AggregateMC(RR, maxrank, type=c("MC4", "MCT"), epsilon = 0.15)

### Arguments

`RR` | An object of class `RepeatedRanking`.
`maxrank` | Due to time- and memory requirements, the computation is limited to a reduced set of candidate genes. A gene is selected as candidate only if at least of one its ranks is smaller than or equal to `maxrank`. The remainder is assigned the rank `maxrank+1` as rank after aggregation.
`type` | Specifies the computation of the matrix of transition probabilities. If `type = "MC4"`, the transition probabilities are forced to be binary, while they may principally range from zero to one if `type = "MCT"`, see DeConde et al. (2006) for details.
`epsilon` | A second parameter concerning the computation of the transition matrix, necessary to guarantee ergodicity and hence existence of a unique stationary distribution of the Markov chain. The value `epsilon = 0.15`, `0 < epsilon < 1`, is recommended in DeConde et al. (2006).

### Value

An object of class AggregatedRanking.

### Author(s)

Martin Slawski
Anne-Laure Boulesteix

### References

DeConde, R. P., Hawley, S., Falcon, S., Clegg, N., Knudsen, B., Etzioni, R. (2006).
Combining results of microarray experiments: a rank aggregation approach. Statistical Applications in Genetics and Molecular Biology 5, 15

### See Also

RepeatRanking, AggregateSVD, AggregatePenalty, AggregateSimple

### Examples

    ## Load toy gene expression data
    data(toydata)
    ### class labels
    yy <- toydata[1,]
    ### gene expression
    xx <- toydata[-1,]
    ### run RankingTstat
    ordT <- RankingTstat(xx, yy, type="unpaired")
    ### Generate Leave-one-out Foldmatrix
    loo <- GenerateFoldMatrix(y = yy, k=1)
    ### Get all rankings
    loor_ordT <- RepeatRanking(ordT, loo)
    ### aggregate rankings
    agg_MC_ordT <- AggregateMC(loor_ordT, type = "MCT", maxrank = 100)
    toplist(agg_MC_ordT)

[Package GeneSelector version 2.30.0 Index]