pairwise.posterior {nem} | R Documentation

## Infers a phenotypic hierarchy edge by edge

### Description

Function `pairwise.posterior` estimates the hierarchy edge by edge. In each step only a pair of nodes is involved and no exhaustive enumeration of model space is needed as in function `score`.

### Usage

    pairwise.posterior(D, type = "mLL", para = NULL, hyperpara = NULL,
        Pe = NULL, Pmlocal = NULL, Pm = NULL, lambda = 0, delta=1, verbose = TRUE)

    ## S3 method for class 'pairwise':
    print(x,...)

### Arguments

`D` | data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
`type` | (1.) marginal likelihood "mLL" (only for cout matrix D), or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 (only for count matrix D), or (3.) "CONTmLL" marginal likelihood for probability matrices, or (4.) "CONTmLLDens" marginal likelihood for probability log-density matrices, or (5.) "CONTmLLRatio" for log-odds ratio matrices
`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
`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

`pairwise.posterior` is a fast(er) heuristic alternative to exhaustive search by the function `score`. For each pair (`A`,`B`) of perturbed genes it chooses between four possible models: `A..B` (unconnected), `A->B` (superset), `A<-B` (subset), or `A<->B` (indistinguishable). The result is the graph built from the maximum aposteriori models for each edge.

`print.pairwise` gives an overview over the 'pairwise' object.

### Value

`graph` | the inferred directed graph (graphNEL object)
`mLL` | marginal likelihood of final model
`pos` | posterior over effect positions
`mappos` | MAP estimate of effect positions
`type` | as used in function call
`para` | as used in function call
`hyperpara` | as used in function call
`lambda` | as in function call
`selected` | selected E-gene subset

### Author(s)

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

### See Also

`score`, `nem`

### Examples

       data("BoutrosRNAi2002")
       res <- pairwise.posterior(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05))

       # plot graph
       plot(res,what="graph")

       # plot posterior over effect positions
       plot(res,what="pos")

       # estimate of effect positions
       res$mappos

[Package nem version 2.4.0 Index]