minNumSeqMutationsTune

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)

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.

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.

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¶

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.

Examples¶

# 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))

         100 150 200 250 300
measured 194 128  92  77  55
inferred 830 896 932 947 969