getRelevantEGenes {nem} | R Documentation |
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.
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)))
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 |
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
# 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")