selectEGenes: Automatic selection of most relevant effect reporters
In cbg-ethz/pcNEM: Probabilistic combinatorial nested effects model

Description Usage Arguments Details Value Author(s) See Also Examples

1. A-priori filtering of effect reporters/E-genes: Select effect reporters, which show a pattern of differential expression across experiments that is expected to be non-random. 2. Automated effect reporters subset selection: Select those effect reporters, which have the highest likelihood under the given network hypothesis.

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filterEGenes(Porig, D, Padj=NULL, ntop=100, fpr=0.05, adjmethod="bonferroni", cutoff=0.05)

getRelevantEGenes(Phi, D, control, nEgenes=min(10*nrow(Phi), nrow(D)))

For method filterEGenes:

`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.
`Padj`	matrix of false positive rates. If not, provided Benjamini-Hochbergs method for false positive rate computation is used.
`ntop`	number of top genes to consider from each knock-down experiment
`fpr`	significance cutoff for the FDR
`adjmethod`	adjustment method for pattern p-values
`cutoff`	significance cutoff for patterns

For method getRelevantEGenes:

`Phi`	adjacency matrix with unit main diagonal
`control`	list of parameters: see `set.default.parameters`
`nEgenes`	no. of E-genes to select

The method filterEGenes performs an a-priori filtering of the complete microarray. It determines how often E-genes are expected to be differentially expressed across experiments just randomly. According to this only E-genes are chosen, which show a pattern of differential expression more often than can be expected by chance.

The method getRelevantEGenes looks for the E-genes, which have the highest likelihood under the given network hypothesis. In case of the scoring type "CONTmLLBayes" these are all E-genes which have a positive contribution to the total log-likelihood. In case of type "CONTmLLMAP" all E-genes not assigned to the "null" S-gene are returned. This involves the prior probability delta/no. S-genes for leaving out an E-gene. For all other cases ("CONTmLL", "FULLmLL", "mLL") the nEgenes E-genes with the highest likelihood under the given network hypothesis are returned.

`I`	index of selected E-genes
`dat`	subset of original data according to I
`patterns`	significant patterns
`nobserved`	no. of cases per observed pattern
`selected`	selected E-genes
`mLL`	marginal likelihood of a phenotypic hierarchy
`pos`	posterior distribution of effect positions in the hierarchy
`mappos`	Maximum a posteriori estimate of effect positions
`LLperGene`	likelihood per selected E-gene

Holger Froehlich

nem, score, mLL, FULLmLL

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

   # enumerate all possible models for 4 genes
   Sgenes = unique(colnames(D))
   models <- enumerate.models(Sgenes)  
   
   getRelevantEGenes(models[[64]], D, control=set.default.parameters(Sgenes, para=c(.13,.05), type="mLL"))