shazam (version 1.2.0)

minNumSeqMutationsTune: Parameter tuning for minNumSeqMutations

Description

minNumSeqMutationsTune helps with picking a threshold value for minNumSeqMutations in createMutabilityMatrix by tabulating the number of 5-mers for which mutability would be computed directly or inferred at various threshold values.

Usage

minNumSeqMutationsTune(mutCount, minNumSeqMutationsRange)

Value

A 2xn matrix, where n is the number of trial values of minNumSeqMutations

supplied in minNumSeqMutationsRange. Each column corresponds to a value in minNumSeqMutationsRange. The rows correspond to the number of 5-mers for which mutability would be computed directly ("measured") and inferred ("inferred"), respectively.

Arguments

mutCount

a vector of length 1024 returned by createMutabilityMatrix with numSeqMutationsOnly=TRUE.

minNumSeqMutationsRange

a number or a vector indicating the value or the range of values of minNumSeqMutations to try.

Details

At a given threshold value of minNumSeqMutations, for a given 5-mer, if the total number of mutations is greater than the threshold, mutability is computed directly. Otherwise, mutability is inferred.

References

1. Yaari G, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol. 2013 4(November):358.

See Also

See argument numSeqMutationsOnly in createMutabilityMatrix for generating the required input vector mutCount. See argument minNumSeqMutations in createMutabilityMatrix for what it does.

Examples

# \donttest{
# Subset example data to one isotype and sample as a demo
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call == "IGHA" & sample_id == "-1h")
set.seed(112)
db <- dplyr::slice_sample(db, n=75)
# Create model using only silent mutations
sub <- createSubstitutionMatrix(db, sequenceColumn="sequence_alignment",
                                germlineColumn="germline_alignment_d_mask",
                                vCallColumn="v_call", 
                                model="s", multipleMutation="independent",
                                returnModel="5mer", numMutationsOnly=FALSE,
                                minNumMutations=20)

# Count the number of mutations in sequences containing each 5-mer
mutCount <- createMutabilityMatrix(db, substitutionModel = sub,
                                   sequenceColumn="sequence_alignment",
                                   germlineColumn="germline_alignment_d_mask",
                                   vCallColumn="v_call",
                                   model="s", multipleMutation="independent",
                                   numSeqMutationsOnly=TRUE)

# Tune minNumSeqMutations
minNumSeqMutationsTune(mutCount, seq(from=100, to=300, by=50))
# }