nem {mnem} | R Documentation

## Implementation of the original NEM

### Description

Infers a signalling pathway from peerturbation experiments.

### Usage

    nem(
      D,
      search = "greedy",
      start = NULL,
      method = "llr",
      marginal = FALSE,
      parallel = NULL,
      reduce = FALSE,
      weights = NULL,
      runs = 1,
      verbose = FALSE,
      redSpace = NULL,
      trans.close = TRUE,
      subtopo = NULL,
      prior = NULL,
      ratio = TRUE,
      domean = TRUE,
      modulesize = 5,
      fpfn = c(0.1, 0.1),
      Rho = NULL,
      logtype = 2,
      modified = FALSE,
      ...
    )

### Arguments

`D` | data matrix with observed genes as rows and knock-down experiments as columns
`search` | either "greedy", "modules" or "exhaustive" (not recommended for more than five S-genes)
`start` | either NULL ("null") or a specific network to start the greedy
`method` | "llr" for log odds or p-values densities or "disc" for binary data
`marginal` | logical to compute the marginal likelihood (TRUE)
`parallel` | NULL for no parallel optimization or an integer for the number of threads
`reduce` | reduce search space (TRUE) for exhaustive search
`weights` | a numeric vector of weights for the columns of D
`runs` | the number of runs for the greedy search
`verbose` | for verbose output (TRUE)
`redSpace` | reduced search space for exhaustive search; see result of exhaustive search with reduce = TRUE
`trans.close` | if TRUE uses the transitive closure of adj
`subtopo` | optional matrix with the subtopology theta as adjacency matrix
`prior` | a prior network matrix for adj
`ratio` | if FALSE uses alternative distance for the model score
`domean` | if TRUE summarizes duplicate columns
`modulesize` | the max number of S-genes included in one module for search = "modules"
`fpfn` | numeric vector of length two with false positive and false negative rates
`Rho` | optional perturbation matrix
`logtype` | log base of the log odds
`modified` | if TRUE, assumes a prepocessed data matrix
`...` | optional parameters for future search methods

### Value

transitively closed matrix or graphNEL

### Author(s)

Martin Pirkl

### Examples

    D <- matrix(rnorm(100*3), 100, 3)
    colnames(D) <- 1:3
    rownames(D) <- 1:100
    adj <- diag(3)
    colnames(adj) <- rownames(adj) <- 1:3
    scoreAdj(D, adj)

[Package mnem version 1.10.0 Index]