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 |

`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 |

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
## 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)
``` |

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