getRelevantEGenes {nem}R Documentation

Automatic selection of most relevant S-genes

Description

1. Selects those E-genes, which have the highest likelihood under the given network hypothesis. 2. Cluster E-genes and select one E-gene from each cluster to reduce the amount of data on the array.

Usage

filterEGenes(Porig, D, ntop=100)

getRelevantEGenes(Phi, D, nEgenes=min(5*ncol(Phi), nrow(D1)), type="mLL", para=NULL, hyperpara=NULL, Pe=NULL, Pm=NULL, lambda=0)

selectEGenes(Phi,D1,D0=NULL,para=NULL,hyperpara=NULL,Pe=NULL,Pm=NULL,lambda=0,type="mLL", nEgenes=min(5*ncol(Phi), nrow(D1)))

Arguments

Porig matrix of raw p-values, typically from the complete array
D data matrix. Columns correspond to the nodes in the silencing scheme. Rows are effect reporters.
ntop number of top genes to consider from each knock-down experiment
Phi adjacency matrix with unit main diagonal
nEgenes no. of E-genes to select
type (1.) marginal likelihood "mLL" (only for cout matrix D), or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 (only for count matrix D), or (3.) "CONTmLL" marginal likelihood for probability matrices, or (4.) "CONTmLLDens" marginal likelihood for probability density matrices, or (5.) "CONTmLLRatio" for log-odds ratio matrices
para Vector with parameters a and b (for "mLL" with count data)
hyperpara Vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"
Pe prior position of effect reporters. Default: uniform over nodes in silencing scheme
Pm prior on model graph (n x n matrix) with entries 0 <= priorPhi[i,j] <= 1 describing the probability of an edge between gene i and gene j.
lambda regularization parameter to incorporate prior assumptions.
D1 (i) count matrix for discrete data: phenotypes x genes. How often did we see an effect after interventions? (ii) matrix describing the probabilities of an effect (iii) probability density matrix discribing the strength of an effect
D0 count matrix: phenotypes x genes. How often did we NOT see an effect after intervention? Not used for continious data

Details

uses mLL or FULLmLL to score each E-gene.

Value

I index of selected E-genes
mLL marginal likelihood of a phenotypic hierarchy
pos posterior distribution of effect positions in the hierarchy
mappos Maximum aposteriori estimate of effect positions

Author(s)

Holger Froehlich

See Also

nem, score, mLL, FULLmLL, enumerate.models

Examples

   # Drosophila RNAi and Microarray Data from Boutros et al, 2002
   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]

   # enumerate all possible models for 4 genes
   models <- enumerate.models(unique(colnames(D)))  
   
   getRelevantEGenes(models[[64]], D, para=c(.13,.05))


[Package nem version 2.2.1 Index]