lmParams: Show and set parameters
In LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

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

Method for objects of class LowMACA. It can show the most important user-definable parameters for a LowMACA analysis and allows to change them.

1 2	lmParams(object) lmParams(object) <- value

object

an object of class LowMaca

value

a named list containing:

mutation_type a character string among: 'missense' , 'truncating' , 'silent' ,'all'. Default 'missense'
tumor_type a character vector or string containing the tumor type barcode of the data in cBioPortal. Default 'all'.
min_mutation_number an integer value describing the minimum number of mutations accepted for a sequence. If a sequence does not harbor a sufficient number of mutations is discarded from the analysis. Default is 1
density_bw either a numeric value or 'auto'. A numeric value is passed directly to the function density while putting 0 will not launch density at all (every position is not aggregated to the surrounded ones). 'auto' will let the simulation decide according to the Silverman's rule of thumb the correct bandwidth. Default is 0.
clustal_cmd path to clustalo executable
use_hmm When analysing Pfam sequences, it is possible to use the Hidden Markov Model (HMM) of the specific Pfam to align the sequences. Default is FALSE.
datum When analysing Pfam sequences, use all the genes that belong to the Pfam to generate the alignment. This creates a unique mapping between individual residues and consensus sequence, disregarding the set of sequences that are selected for the analysis. Default is FALSE.

LowMACA is a suite of tool that analyze conserved mutations, so it looks for clusters of gain of function alterations. With 'missense' mutation_type we intend all those mutations that change the original DNA but do not create stop codon nor alter the reading frame (these mutations are collectively defined as 'truncating' mutations). In addition we let the possibility to also choose 'silent' mutations even though they are currently not supported by the cBioPortal. To see all the available tumor types to run a LowMACA analysis, simply run showTumorType. The parameter density_bw has a strong effect on the statistical analysis of LowMACA. With the default bandwidth (0), the Shannon entropy calculation becomes descrete, while the continuos version is used in all the other cases.

If lmParams is used as a show method it returns a named list of 5 elements: mutation_type='missense' , tumor_type='all' , min_mutation_number=1 , density_bw=0 , clustal_cmd='clustalo'

Stefano de Pretis , Giorgio Melloni

showTumorType getMutations entropy density

#Construct a LowMACA object
lm <- newLowMACA(pfam="PF12906")
#Show default parameters
lmParams(lm)
#Change all parameters
lmParams(lm) <- list(mutation_type='all' 
                    , tumor_type=c('skcm','brca') 
                    , min_mutation_number=0 
                    , density_bw=0
                    , clustal_cmd='clustalo'
                    , use_hmm=FALSE
                    , datum=FALSE)
#Change just one parameter
lmParams(lm)[['tumor_type']] <- 'prad'