createTargetingMatrix {shazam} | R Documentation |
Calculates a targeting rate matrix
Description
createTargetingMatrix
calculates the targeting model matrix as the
combined probability of mutability and substitution.
Usage
createTargetingMatrix(substitutionModel, mutabilityModel)
Arguments
substitutionModel |
matrix of 5-mers substitution rates built by createSubstitutionMatrix or extendSubstitutionMatrix. |
mutabilityModel |
vector of 5-mers mutability rates built by createMutabilityMatrix or extendMutabilityMatrix. |
Details
Targeting rates are calculated by multiplying the normalized mutability rate by the normalized substitution rates for each individual 5-mer.
Value
A TargetingMatrix
with the same dimensions as the input substitutionModel
containing normalized targeting probabilities for each 5-mer motif with
row names defining the center nucleotide and column names defining the
5-mer nucleotide sequence.
If the input mutabilityModel
is of class MutabilityModel
, then the output
TargetingMatrix
will carry over the input numMutS
and numMutR
slots.
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
createSubstitutionMatrix, extendSubstitutionMatrix, createMutabilityMatrix, extendMutabilityMatrix, TargetingMatrix, createTargetingModel
Examples
# Subset example data to 50 sequences, of one isotype and sample as a demo
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call == "IGHA" & sample_id == "-1h")[1:50,]
# Create 4x1024 models using only silent mutations
sub_model <- createSubstitutionMatrix(db, model="s", sequenceColumn="sequence_alignment",
germlineColumn="germline_alignment_d_mask",
vCallColumn="v_call")
mut_model <- createMutabilityMatrix(db, sub_model, model="s",
sequenceColumn="sequence_alignment",
germlineColumn="germline_alignment_d_mask",
vCallColumn="v_call")
# Extend substitution and mutability to including Ns (5x3125 model)
sub_model <- extendSubstitutionMatrix(sub_model)
mut_model <- extendMutabilityMatrix(mut_model)
# Create targeting model from substitution and mutability
tar_model <- createTargetingMatrix(sub_model, mut_model)