LowMACA-class: Class '"LowMACA"'
In LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

Description Objects from the Class Constructor Slots Methods Author(s) References See Also Examples

LowMACA class object describing the properties of mutations mapped on pfam domains or proteins

Objects can be created by calls of the form newLowMACA(genes, pfam).

newLowMACA(genes=character_vector , pfam=character_vector)

arguments

Object of class "list" with 6 elements:

genes : vector of selected genes for the analysis in Hugo names format. NULL if mode="pfam".
pfam : vector of selected domains for the analysis in pfam ids format. NULL if mode="genes".
input : data.frame describing the input data as gene symbols, pfam ids, entrez ids, envelope start and end of the domain relative to the protein, name of the canonical protein in uniprot format, amino acidic sequence.
mode : character. automatically set by the constructor as either "pfam" or "genes". If pfam=NULL then mode="genes", "pfam" otherwise.
params : named list of starting parameters for the LowMaca analysis. Call lmParams(object) to show default. See lmParams for further details.
parallelize : named list of logicals. getMutations=FALSE is the default for the getMutations method and makeAlignment=TRUE is the default for the alignSequences method. See parallelize for further details.

alignment

Object of class "list" with 4 elements:

ALIGNMENT : data.frame of the result of the alignment. Every row represents a position of a sequence and the relative mapping to the consensus sequence.
SCORE : list of two elements. DIST_MAT is a matrix of pairwise similarities between sequences as described by clustalo. SUMMARY_SCORE is a dataframe of summary descriptive statistics of the DIST_MAT matrix
CLUSTAL : an object of class MultipleAlignment-class from package Biostrings
df : a data.frame describing the consensus sequence, its per-position degree of conservation and its mutations null profile density. See entropy and lmPlot for further details

mutations

Object of class "list" with 3 elements:

data : data.frame derived from the query to the cBioPortal query, cgdsr-getMutationData Every row represents a mutation stratified by position, gene and tumor type.
freq : data.frame of absolute frequency of mutation stratified by gene and tumor type.
aligned : matrix representing the number of mutations at every position in the consensus sequence (columns) and in each original sequence (rows)

entropy

Object of class "list" with 5 elements:

bw : numeric value. user defined bandwidth for the function entropy
uniform : function that generate the uniform null profile
absval : numeric value. Shannon entropy of the mutation data profile according to the defined bandwidth
log10pval : numeric value. pvalue of the entropy test in -log10 scale
pvalue : numeric value. pvalue of the entropy test

alignSequences: alignSequences(object = "LowMACA"): ...
bpAll: bpAll(object = "LowMACA"): ...
entropy: entropy(object = "LowMACA"): ...
getMutations: getMutations(object = "LowMACA"): ...
lfm: lfm(object = "LowMACA"): ...
lmPlot: lmPlot(object = "LowMACA"): ...
mapMutations: mapMutations(object = "LowMACA"): ...
nullProfile: signature(object = "LowMACA"): ...
parallelize: parallelize(object = "LowMACA"): ...
parallelize<-: signature(object = "LowMACA"): ...
lmParams: params(x = "LowMACA"): ...
lmParams<-: signature(object = "LowMACA"): ...
protter: protter(object = "LowMACA"): ...
setup: setup(object = "LowMACA"): ...
show: show(object = "LowMACA"): ...
lfmSingleSequence: lfmSingleSequence(object = "LowMACA"): ...
lmPlotSingleSequence: lmPlotSingleSequence(object = "LowMACA"): ...

Stefano de Pretis, Giorgio Melloni

LowMACA website

newLowMACA

#ANALYSIS OF SOME OF THE PROTEINS THAT SHARE THE HOMEOBOX DOMAIN
#Genes to analyze
Genes <- c("ADNP","ALX1","ALX4","ARGFX","CDX4","CRX"
  		,"CUX1","CUX2","DBX2","DLX5","DMBX1","DRGX"
			,"DUXA","ESX1","EVX2","HDX","HLX","HNF1A"
			,"HOXA1","HOXA2","HOXA3","HOXA5","HOXB1","HOXB3"
			,"HOXD3","ISL1","ISX","LHX8")
#Pfam to analyze
Pfam <- "PF00046"
#Construct a new LowMACA object
lm <- newLowMACA(genes=Genes , pfam=Pfam)
#Change some parameters
lmParams(lm)[['tumor_type']] <- c("skcm" , "stad" , "ucec" , "luad" , "lusc" , "coadread" , "brca")
lmParams(lm)[['min_mutation_number']] <- 1
lmParams(lm)[['density_bw']] <- 0
#Run if you have clustalo installed
lm <- setup(lm)
#Calculate staistics
lm <- entropy(lm)
#Retrieve original mutations
lfm(lm)
#Plot
bpAll(lm)
lmPlot(lm)
protter(lm)