triples.posterior {nem} | R Documentation |
Function triples.posterior
estimates the hierarchy triple-wise. In each step only a triple of nodes
is involved and no exhaustive enumeration of model space is needed as in function score
.
triples.posterior(D, type="mLL",para=NULL, hyperpara=NULL,Pe=NULL,Pmlocal=NULL,Pm=NULL,lambda=0,delta=1, triples.thrsh=.5,verbose=TRUE) ## S3 method for class 'triples': print(x,...)
D |
data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes. |
type |
see nem |
para |
vector with parameters a and b for "mLL", if count matrices are used |
hyperpara |
vector with hyperparameters a0, b0, a1, b1 for "FULLmLL" |
Pe |
prior position of effect reporters. Default: uniform over nodes in hierarchy |
Pmlocal |
local model prior for the four models tested at each node: a vector of length 4 with positive entries summing to one |
triples.thrsh |
threshold used when combining tripel models for each edge. Default: only edges appearing in more than half of triples are included in the final graph. |
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 (CONTmLLRatio only) |
verbose |
do you want to see progress statements printed or not? Default: TRUE |
x |
nem object |
... |
other arguments to pass |
triples.posterior
is an alternative to exhaustive search
by the function score
and more accurate than pairwise.posterior
.
For each triple of perturbed genes
it chooses between the 29 possible models. It then uses model averaging to combine the triple-models into a final graph.
print.triples
gives an overview over the 'triples' object.
nem object
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>
Markowetz F, Kostka D, Troyanskaya OG, Spang R: Nested effects models for high-dimensional phenotyping screens. Bioinformatics. 2007; 23(13):i305-12.
data("BoutrosRNAi2002") res <- nem(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05),inference="triples") # plot graph plot(res,what="graph") # plot posterior over effect positions plot(res,what="pos") # estimate of effect positions res$mappos