shazam (version 0.1.2)

createSubstitutionMatrix: Builds a substitution model

Description

createSubstitutionMatrix builds a 5-mer nucleotide substitution model by counting the number of substitution mutations occuring in the center position for all 5-mer motifs.

Usage

createSubstitutionMatrix(db, model = c("RS", "S"), sequenceColumn = "SEQUENCE_IMGT", germlineColumn = "GERMLINE_IMGT_D_MASK", vCallColumn = "V_CALL", multipleMutation = c("independent", "ignore"), returnModel = c("5mer", "1mer", "1mer_raw"), minNumMutations = 50)

Arguments

db

data.frame containing sequence data.

model

type of model to create. The default model, "RS", creates a model by counting both replacement and silent mutations. The "S" specification builds a model by counting only silent mutations.

sequenceColumn

name of the column containing IMGT-gapped sample sequences.

germlineColumn

name of the column containing IMGT-gapped germline sequences.

vCallColumn

name of the column containing the V-segment allele call.

multipleMutation

string specifying how to handle multiple mutations occuring within the same 5-mer. If "independent" then multiple mutations within the same 5-mer are counted indepedently. If "ignore" then 5-mers with multiple mutations are excluded from the total mutation tally.

returnModel

string specifying what type of model to return; one of c("5mer", "1mer", "1mer_raw"). If "5mer" (the default) then a 5-mer nucleotide context model is returned. If "1mer" or "1mer_raw" then a single nucleotide substitution matrix (no context) is returned; where "1mer_raw" is the unnormalized version of the "1mer" model. Note, neither 1-mer model may be used as input to createMutabilityMatrix.

minNumMutations

minimum number of mutations required to compute the 5-mer substitution rates. If the number of mutations for a 5-mer is below this threshold, its substitution rates will be estimated from neighboring 5-mers. Default is 50.

Value

A 4x1024 matrix of column normalized substitution rates for each 5-mer motif with row names defining the center nucleotide, one of c("A", "C", "G", "T"), and column names defining the 5-mer nucleotide sequence.

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

Other targeting model functions: calcTargetingDistance, createMutabilityMatrix, createTargetingMatrix, createTargetingModel, extendMutabilityMatrix, extendSubstitutionMatrix, plotMutability, writeTargetingDistance

Examples

# Subset example data to one isotype and sample as a demo
db <- subset(InfluenzaDb, CPRIMER == "IGHA" & BARCODE == "RL014")

# Create model using only silent mutations and ignore multiple mutations
sub <- createSubstitutionMatrix(db, model="S", multipleMutation="ignore")