LCA: Localised Co-dependency Analysis
In LCA: Localised Co-Dependency Analysis

Description Usage Arguments Details Value Author(s) Examples

Performs Localised Co-dependency Analysis

1 2	LCA(x,PTLmodel,clique,seed.row,combine.method="Fisher", adjust.method="BH",comparison.alpha=0.05)

`x`	Numeric data input array, standardised to range (0,1)
`PTLmodel`	List with named elements `alpha`, `beta` and `gamma` specifying PTL parameters
`clique`	Numeric vector specifying which columns of data table represent entities defining the clique across which to evaluate co-dependency
`seed.row`	Numeric value specifying which row of data table to use as 'seed' feature with which to evaluate co-dependency
`combine.method`	Character specifying which method to use for combining individual LCD estimates. One of "Fisher" or "Inverse Product".
`adjust.method`	Character specifying which method to use for multiple testing adjustment of significance estimates. See `p.adjust` for further details.
`comparison.alpha`	Significance level threshold for including objects in the set to be used for evaluating LCD significance estimates for a given pair of features in a given clique.

Function to evaluate LCD, within the members of clique, for all features in a dataset against the feature represented by seed.row.

List with elements:

`LCD`	Data frame giving across-clique LCD significance estimates for each feature in the dataset, as both unadjusted p-value and adjusted for multiple testing.
`combinations`	An array detailing the individual pair-wise LCD tests performed amongst members of the clique, which were combined to give the overall significance estimates

Ed Curry e.curry@imperial.ac.uk

suppressWarnings(RNGversion("3.5.0"))
## create a data matrix

x <- array(runif(1200),dim=c(40,12))

## implant similarity across a 'clique'

clique.cols <- sample(ncol(x),4)
x[,clique.cols] <- x[,clique.cols] + rnorm(nrow(x))

## scale x to (0,1)

x[x<0] <- 0
x[x>1] <- 1

## choose a 'seed' feature and some partner

seed.row <- sample(nrow(x),1)
partner.row <- sample(setdiff(c(1:nrow(x)),seed.row),1)

x[c(seed.row,partner.row),clique.cols] <- x[c(seed.row,partner.row),clique.cols] + 
rep(rnorm(length(clique.cols)),each=2)

## calibrate PTL models to dataset

PTL.fit <- fitPTLmodel(x,nPairs=15)

## evaluate LCD between 'seed' feature and all other features
 
LCA.result <- LCA(x,PTLmodel=PTL.fit,clique=clique.cols,seed.row=seed.row)
## Not run: head(LCA.result$LCD)