| minNumMutationsTune {shazam} | R Documentation |
Parameter tuning for minNumMutations
Description
minNumMutationsTune helps with picking a threshold value for minNumMutations
in createSubstitutionMatrix by tabulating the number of 5-mers for which
substitution rates would be computed directly or inferred at various threshold values.
Usage
minNumMutationsTune(subCount, minNumMutationsRange)
Arguments
subCount |
|
minNumMutationsRange |
a number or a vector indicating the value or range of values
of |
Details
At a given threshold value of minNumMutations, for a given 5-mer,
if the total number of mutations is greater than the threshold and there
are mutations to every other base, substitution rates are computed directly
for the 5-mer using its mutations. Otherwise, mutations from 5-mers with
the same inner 3-mer as the 5-mer of interest are aggregated. If the number
of such mutations is greater than the threshold and there are mutations to
every other base, these mutations are used for inferring the substitution
rates for the 5-mer of interest; if not, mutations from all 5-mers with the
same center nucleotide are aggregated and used for inferring the substitution
rates for the 5-mer of interest (i.e. the 1-mer model).
Value
A 3xn matrix, where n is the number of trial values of minNumMutations
supplied in minNumMutationsRange. Each column corresponds to a value
in minNumMutationsRange. The rows correspond to the number of 5-mers
for which substitution rates would be computed directly using the 5-mer itself
("5mer"), using its inner 3-mer ("3mer"), and using the central
1-mer ("1mer"), respectively.
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.
See Also
See argument numMutationsOnly in createSubstitutionMatrix
for generating the required input data.frame subCount.
See argument minNumMutations in createSubstitutionMatrix
for what it does.
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")
# Count the number of mutations per 5-mer
subCount <- createSubstitutionMatrix(db, sequenceColumn="sequence_alignment",
germlineColumn="germline_alignment_d_mask",
vCallColumn="v_call",
model="s", multipleMutation="independent",
returnModel="5mer", numMutationsOnly=TRUE)
# Tune minNumMutations
minNumMutationsTune(subCount, seq(from=10, to=80, by=10))