epiNEM {epiNEM} | R Documentation

## Epistatic NEMs - main function.

### Description

This function contains the inference algorithm to learn logical networks from knock-down data including double knock-downs.

### Usage

    epiNEM(
      filename = "random",
      method = "greedy",
      nIterations = 10,
      nModels = 0,
      random = list(single = 4, double = 1, reporters = 100, FPrate = 0.1, FNrate = 0.1,
        replicates = 1),
      ltype = "marginal",
      para = c(0.13, 0.05),
      init = NULL
    )

### Arguments

`filename` | A binary, tab-delimited matrix. Columns: single and double knockdowns. Rows: genes showing effect or not? Default: random; artificial data is generated to 'random' specifications
`method` | greedy or exhaustive search. Default: greedy
`nIterations` | number of iterations. Default: 10
`nModels` | number of Models. Default: 0
`random` | list specifying how the data should be generated: no. of single mutants, no. of double mutants, no. of reporterGenes, FP-rate, FN-rate, no. of replicates
`ltype` | likelihood either "marginal" or "maximum"
`para` | false positive and false negative rates
`init` | adjacency matrix to initialise the greedy search

### Value

List object with an adjacency matrix denoting the network, the model of the silencing scheme (rows are knock-downs, columns are signalling genes), a string with the inferred logial gates, a column indices denoting position of logical gates, the log transformed likelihood and the effect reporter distribution (rows are the signalling genes including the null node).

### Author(s)

Madeline Diekmann

### See Also

nem

### Examples

    data <- matrix(sample(c(0,1), 100*4, replace = TRUE), 100, 4)
    colnames(data) <- c("A", "A.B", "B", "C")
    rownames(data) <- paste("E", 1:100, sep = "_")
    res <- epiNEM(data, method = "exhaustive")
    plot(res)

[Package epiNEM version 1.14.1 Index]