findSignifCliques: Tests all maximal cliques for mutual exclusivity
In cbg-ethz/TiMEx: Finding Mutually Exclusive Groups of Alterations in Cancer Datasets

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

findSignifCliques performs step 3 of the TiMEx procedure, namely tests all candidate maximal cliques for mutual exclusivity and reports the significant ones.

1	findSignifCliques(mat, mcStruct, groupPvalue)

`mat`	binary alteration matrix, with rows representing patients and columns representing genes
`mcStruct`	list containing maximal cliques, as returned by `doMaxCliques`
`groupPvalue`	threshold for the corrected p-value of the groups, lower than which cliques are significant (real number between 0 and 1). Default is 0.1.

This function displays progress messages, namely the size of the clique currently being tested, and the number of cliques to test.

Note that sequentially performing steps 1, 2, and 3 of the TiMEx procedure (functions analyzePairs, doMaxCliques, and findSignifCliques) is equivalent to simply running the function TiMEx.

list consisting of:

genesSignif list of significantly mutually exclusive groups, as gene names, sorted by corrected p-value. The list contains as many elements as identified lengths of groups. For example, genesSignif[[2]] is a list containing the gene names of the significant groups of size 2. Each list of this type further has two elements, fdr and bonf, corresponding to different multiple testing correction methods. Each element is a matrix, in which rows represent gene names of significantly mutually exclusive groups.
idxSignif list of significantly mutually exclusive groups, as indices in the input matrix, sorted by corrected p-value. The list contains as many elements as identified lengths of groups. For example, idxSignif[[2]] is a list containing the indices of the significant groups of size 2. Each list of this type further has two elements, fdr and bonf, corresponding to different multiple testing correction methods. Each element is a matrix, in which rows represent indices of significantly mutually exclusive groups.
pvals list of corrected significant p-values corresponding to the tested cliques, ordered ascendingly. The list contains as many elements as identified lengths of significant groups. For example, pvals[[2]] is a list containing the p-values of the significant maximal cliques of size 2. Each list of this type further has two elements, fdr and bonf, corresponding to different multiple testing correction methods. Each element is a vector, of length the number of significant maximal cliques of a given size.
posSignif list of positions of the significant groups in the input list of maximal cliques, ordered ascendingly by corrected p-value. The list contains as many elements as identified lengths of significant groups. For example, posSignif[[2]] is a list containing the positions of the significant groups of size 2. Each list of this type further has two elements, fdr and bonf, corresponding to different multiple correction methods. Each element is a vector, of length the number of significant maximal cliques of a given size.
MusGroup list of inferred mu values corresponding to the tested cliques, ordered ascendingly by the corresponding corrected p-value. The list contains as many elements as identified lengths of significant groups. For example, MusGroup[[2]] is a list containing the mu values of the significant maximal cliques of size 2. Each list of this type further has two elements, fdr and bonf, corresponding to different multiple testing correction methods. Each element is a vector, of length the number of significant maximal cliques of a given size.
mcStruct input structure of maximal cliques to be tested for mutual exclusivity, as returned by doMaxCliques
matrix input binary alteration matrix
groupPvalue input threshold for the corrected p-value, lower than which cliques are significant

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; doMaxCliques for step 2 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. The data structures ovarianOutput, breastOutput, gbmDendrixOutput, and gbmMuexOutput are examples of structures resulting after running TiMEx on large cancer datasets.

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

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

# Then, test all maximal cliques for mutual exclusivity and report the 
# significant ones, based on a corrected p-value threshold of 0.1 (default).
ovarianMEgroups<-findSignifCliques(ovarian,ovarianMaxCliques)