getRelevantEGenes {nem} | R Documentation

## Automatic selection of most relevant effect reporters

### Description

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.

### Usage

    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)))

### Arguments

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

### Details

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.

### Value

`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

### Author(s)

Holger Froehlich

### See Also

`nem`, `score`, `mLL`, `FULLmLL`

### Examples

       # 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"))

[Package nem version 2.56.0 Index]