getSimData {dcanr} | R Documentation

## Get data and conditions from a given knock-down (KD)

### Description

Retrieves the simulated expression matrix and sample classification for a specific knock-down experiment.

### Usage

    getSimData(simulation, cond.name = NULL, full = FALSE)

    getConditionNames(simulation)

    getTrueNetwork(simulation, cond.name = NULL,
      truth.type = c("association", "influence", "direct"), full = FALSE)

### Arguments

`simulation` | a list, storing data and results generated from simulations
`cond.name` | a character, indicating the knock-down to use to derive conditions. Multiple knock-downs (KDs) are performed per simulation. If `NULL`, the first KD is chosen
`full` | a logical, indicating whether genes associated with the condition should be excluded. Defaults to `FALSE` and is recommended
`truth.type` | a character, specifying which level of the true network to retrieve: 'association' (default), 'influence' or 'direct'

### Details

Genes discarded when `full` is `FALSE` are those that are solely dependent on the condition. These genes are discarded from the analysis to focus on those that are differentially co-expressed, not coordinately co-expressed.

The names of all genes knocked-out can be retrieved using `getConditionNames`.

The direct, influence and association networks represent different levels of true differential networks. The direct network contains differential regulatory interactions present in the original network. The influence network includes upstream interactions and the association network includes non-causative differential interactions.

### Value

a list, containing `emat`, a matrix representing the expression data, `condition`, a numeric containing the classification of samples, and , `condition_c`, a numeric containing the expression levels of the KD gene (continuous condition) for `getSimData`; the names of all genes that are KD for `getConditionNames`; and an adjacency matrix for `getTrueNetwork`.

### Functions

- `getSimData`: get the expression matrix and sample classification

- `getConditionNames`: get names of the conditions (KDs)

- `getTrueNetwork`: get the true differential network

### See Also

`dcScore`

### Examples

    data(sim102)
    KDs <- getConditionNames(sim102)

    #get simulated data
    simdata <- getSimData(sim102, KDs[2])
    cond <- simdata$condition
    emat <- simdata$emat
    zscores <- dcScore(emat, cond)

    #get the true network to evaluate against
    truenet <- getTrueNetwork(sim102, KDs[2], truth.type = 'association')

[Package dcanr version 1.0.0 Index]