getRelevantEGenes {nem} | R Documentation

## Automatic selection of most relevant E-genes

### Description

1. A-priori filtering of E-genes: Select E-genes, which show a pattern of differential expression across experiments that is expected to be non-random. 2. Automated E-gene subset selection: Select those E-genes, 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, para=NULL, hyperpara=NULL,Pe=NULL,Pm=NULL,lambda=0, delta=1, type="CONTmLLDens", 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
`Phi` | adjacency matrix with unit main diagonal
`type` | `mLL` or `FULLmLL` or `CONTmLL` or `CONTmLLBayes` or `CONTmLLMAP`. `CONTmLLDens` and `CONTmLLRatio` are identical to `CONTmLLBayes` and `CONTmLLMAP` and are still supported for compatibility reasons, see `nem`.
`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.
`delta` | regularization parameter for automated E-gene subset selection (CONTmLLMAP only)
`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
       models <- enumerate.models(unique(colnames(D)))

       getRelevantEGenes(models[[64]], D, para=c(.13,.05), type="mLL")

[Package nem version 2.6.0 Index]