triples.posterior {nem} | R Documentation

## Infers a phenotypic hierarchy from triples

### Description

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`.

### Usage

    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,...)

### Arguments

`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

### Details

`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.

### Value

nem object

### Author(s)

Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

### References

Markowetz F, Kostka D, Troyanskaya OG, Spang R: Nested effects models for high-dimensional phenotyping screens. Bioinformatics. 2007; 23(13):i305-12.

### See Also

`score`, `nem`

### Examples

       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

[Package nem version 2.6.0 Index]