getSimData: Get data and conditions from a given knock-down (KD)
In dcanr: Differential co-expression/association network analysis

Description Usage Arguments Details Value Functions See Also Examples

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

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

getConditionNames(simulation)

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

`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'

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.

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.

getSimData: get the expression matrix and sample classification
getConditionNames: get names of the conditions (KDs)
getTrueNetwork: get the true differential network

dcScore

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')