AggregateMC {GeneSelector} | R Documentation |
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).
AggregateMC(RR, maxrank, type=c("MC4", "MCT"), epsilon = 0.15)
RR |
An object of class |
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 |
type |
Specifies the computation of the matrix of transition probabilities. If |
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
|
An object of class AggregatedRanking.
Martin Slawski
Anne-Laure Boulesteix
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
RepeatRanking, AggregateSVD, AggregatePenalty, AggregateSimple
## 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)