Function reference • select

All functions
`APC()`	APC correction. Core version, unaware of any subdivision of the dataset.
`APC.block()`	APC correction. Extended version, aware of subdivision of the dataset.
`FDR_analysis()`	Perform FDR analysis and determine optimal Pvalue threshold
`FDR_threshold()`	Determine optimal P value thresholds to have a FDR.thresh (default: 0.1) FDR
`TP.FP.stat()`	Derive TP and FP stats for APC based on FDR scores for a specific threshold
`TP.FP.stats()`	Derive TP and FP stats for APC based on FDR scores for a range of thresholds
`add.ASC()`	Calculate ASC score
`add.patterns()`	Add some other statistics
`add.r.info()`	Add some other statistics
`al.pairwise.alteration.ME_p()`	Compute the ME Pvalue starting from overlap statistics
`al.pairwise.alteration.custom_measure()`	Generic interface to calculate pairwise scores between all the pairs of alterations. Used as front-end for MI measures
`al.pairwise.alteration.stats()`	Compute overlap statistics for the AL
`al.stats()`	Compute simple statistics for the AL, including accurate statistics on each sample/alteration block
`am.pairwise.alteration.coverage()`	Compute pairwise coverage
`am.pairwise.alteration.overlap()`	Compute pairwise overlap
`am.stats()`	Calculate simple statistics for an am
`binary.MI()`	Binary Mutual Information. Works on single units, vectors or matrices
`binary.MI_debug()`	Debug version of Binary Mutual Information. Works on single units, vectors or matrices.
`birewireBlocEvents()`	Generate random binary matrix
`build.interaction.table()`	Build interaction table
`corrected.binary.MI()`	Weighted MI measure
`establish_APC_threshold()`	Calculate ASC score
`estimate_FDR()`	Core FDR estimation
`estimate_FDR_by_type()`	Interaction type aware FDR estimation
`estimate_random_Pval()`	Get random P.values
`f_corr_fix_N_consistency()`	Consistency enforcer for correction function for weight MI measure
`f_sigmoid_max_block_correction()`	Block aware correction function for weight MI measure
`f_sigmoid_max_correction()`	Correction function for weight MI measure
`filter.al()`	Filter AL
`gen.random.al()`	Generate random alteration landscapes
`gen.random.am()`	Generate random alteration matrix. Enables parallel calculation
`get.blocks()`	Get sample/alteration blocks
`get_thresh.2()`	Establish which threshold is the best, using the 95% of FP exclusion criterion
`get_vetos()`	Build naive veto list by vetoing interactions of CNAs within the same chromosome
`interaction.table()`	Assemble a table of pairwise interactions between alterations. Given N alterations, the table will contain N(N-1)/2 rows.
`lis2mat()`	Transform a pairwise table column into tabular form, taking care of NAs
`listmat_to_elemvec()`	Convert a list of m matrices (n x n) to n lists of n vectors of m elements
`luad_data`	Lung adenocarcinoma data from TCGA cohort
`new.AL()`	Init AL object
`new.ALS()`	Create an Alteration Landscape Statistics (ALS) object
`new.AMS()`	Create an Alteration Matrix Statistics (AMS) object
`pairwise.p_MI()`	Compute P vlaue for custom score
`prune_invariants()`	Remove interactions with observed overlap strictly similar to expected overlap
`prune_vetoed()`	Remove vetoed interactions (eg CNA between the same chromosome)
`r.al.pairwise.alteration.custom_measure()`	Compute custom score for the random ams
`r.al.pairwise.alteration.stats()`	Compute overlap statistics for the random ams
`select()`	SELECT analysis
`select.internal()`	internal SELECT routine
`select.on.AL()`	SELECT analysis on an AL with null model

Reference

All functions