doMaxCliques: Identifies maximal cliques from pairwise testing information
In cbg-ethz/TiMEx: Finding Mutually Exclusive Groups of Alterations in Cancer Datasets

Description Usage Arguments Details Value Author(s) References See Also Examples

doMaxCliques performs step 2 of the TiMEx procedure: identifies maximal cliques using information from pairwise testing. The maximal clique detection routine only uses the connections between gene pairs which satisfy the thresholds on mu (pairMu) and pvalue (pairPvalue).

1	doMaxCliques(pairs, pairMu, pairPvalue)

`pairs`	list resulting after pairwise testing, as returned by `analyzePairs`
`pairMu`	pair-level threshold on mu (real number between 0 and 1). Default is 0.5.
`pairPvalue`	pair-level threshold on p-value (real number between 0 and 1). Default is 0.01.

In the second step, the TiMEx procedure for identifying mutually exclusive groups of alterations detects maximal cliques using pairwise testing information from step 1. A graph is constructed, in which genes are vertices, and an edge is drawn between any pair (i,j) if both the estimated intensity of mutual exclusivity and the computed p-value satisfy the chosen thresholds pairMu and pairPvalue. Maximal cliques are detected on this graph.

The two thresholds can be set by the user, and are recommended to be chosen based on the sensitivity and specificity levels to which they correspond, as assessed in simulated data. For details, see 'TiMEx: A Waiting Time Model For Mutually Exclusive Cancer Alterations', by Constantinescu et al. (2015). The default values are 0.5 for pairMu and 0.01 for pairPvalue.

This function needs functions from the packages RBGL and igraph to run.

list consisting of:

detectedLengths vector of lengths of the identified maximal cliques.
idxInCliques list with as many elements as lengths of the identified maximal cliques. Each element of the list is a matrix, in which each row represents the indices of genes in an identified maximal clique.
genesInCliques list with as many elements as lengths of the identified maximal cliques. Each element of the list is a matrix, in which each row represents the names of genes in an identified maximal clique.
noMaxCliques vector of numbers of identified maximal cliques corresponding to each length present in the field detectedLengths.
Mus list of two elements: OrderedGenesInCliques and OrderedIdxInCliques, which have the same structure as the elements genesInCliques and idxInCliques. The only difference is that the identified maximal cliques are now ordered by their averge pairwise mu.
pairMu input pair-level threshold on mu.
pairPvalue input pair-level threshold on p-value.

Simona Cristea, scristea@jimmy.harvard.edu

Constantinescu et al.: TiMEx: A Waiting Time Model for Mutually Exclusive Cancer Alterations. Bioinformatics (2015).

analyzePairs for step 1 of the TiMEx procedure; findSignifCliques for step 3 of the TiMEx procedure; the wrapper function TiMEx for combining these three steps, and identifying mutually exclusive groups in a binary dataset with the TiMEx model.

# First, test all pairs from the ovarian cancer dataset for mutual 
# exclusivity (take approximately 5 minutes) 
data(ovarian)
ovarianPairs<-analyzePairs(ovarian)

# Then, identify all maximal cliques using the default thresholds
ovarianMaxCliques<-doMaxCliques(ovarianPairs)