CELLector.mostSupported_CFEs: Most recurrent combinations of Cancer Functional Events

In najha/CELLector: Genomics guided selection of cancer cell lines

Most recurrent combinations of Cancer Functional Events

Description

This function identifies the most frequent combination of cancer functional events (CFEs) in a large cohort of cancer patients.

Usage

CELLector.mostSupported_CFEs(transactions,
                             minSupport = 0.05,
                             minlen = 1,
                             maxLen = 10)

Arguments

transactions	A named binary matrix with CFEs on the rows, samples on the columns and entries specifying the presence/absence of a given CFE in a given sample: the transactions object.
minSupport	The minimal support that a combination of CFEs must have, i.e. the minimal ratio of samples in which the CFEs must be observed simoultanously, in order to be considered in the analysis.
minlen	The minimal length of a combination of CFEs (of how many indivudal CFE it needs to be composed) in order to be considered in the analysis (1 by default).
maxLen	The maximal length of a combination of CFEs (the maximal number of indivudal CFEs) in order to be considered in the analysis (10 by default).

Details

This function uses the eclat function from the R package arules.

Value

A list with the following fields:

MSIS	A string or a vector of strings (depending on the argument minlen) specifying the CFE (or the combination of individual CFEs) that is the most frequently observed (simultaneously across the samples in input)
SUPPORT	The ratio of samples where the combination of CFEs in MSIS is obaserved on the total number of samples, i.e. number of columns in the transactions argument
absSUPPORT	The number of samples where the combination of CFEs in MSIS is obaserved
supportingSamples	The identifiers of the samples supporting MSIS, i.e. the names of the columns of transactions, in which the entries corresponding to MSIS rows are equal to 1.

Author(s)

Hanna Najgebauer and Francesco Iorio

References

Najgebauer et al., CELLector: Genomics Guided Selection of Cancer in vitro Models.

doi:10.1101/275032

Examples

data(CELLector.PrimTum.BEMs)
RES<-CELLector.mostSupported_CFEs(transactions = t(CELLector.PrimTum.BEMs$COREAD),
                             minlen = 2)

najha/CELLector documentation built on Feb. 8, 2023, 5:35 a.m.