shazam (version 0.1.4)

calcBaseline: Calculate the BASELINe PDFs

Description

calcBaseline calculates the BASELINe posterior probability density functions (PDFs) for sequences in the given Change-O data.frame.

Usage

calcBaseline(db, sequenceColumn = "SEQUENCE_IMGT", germlineColumn = "GERMLINE_IMGT_D_MASK", testStatistic = c("local", "focused", "imbalanced"), regionDefinition = NULL, targetingModel = HS5FModel, mutationDefinition = NULL, calcStats = FALSE, nproc = 1)

Arguments

db

data.frame containing sequence data and annotations.

sequenceColumn

character name of the column in db containing input sequences.

germlineColumn

character name of the column in db containing germline sequences.

testStatistic

character indicating the statistical framework used to test for selection. One of c("local", "focused", "imbalanced").

regionDefinition

RegionDefinition object defining the regions and boundaries of the Ig sequences.

targetingModel

TargetingModel object. Default is HS5FModel.

mutationDefinition

MutationDefinition object defining replacement and silent mutation criteria. If NULL then replacement and silent are determined by exact amino acid identity. Note, if the input data.frame already contains observed and expected mutation frequency columns then mutations will not be recalculated and this argument will be ignored.

calcStats

logical indicating whether or not to calculate the summary statistics data.frame stored in the stats slot of a Baseline object.

nproc

number of cores to distribute the operation over. If nproc=0 then the cluster has already been set and will not be reset.

Value

A Baseline object containing the modified db and BASELINe posterior probability density functions (PDF) for each of the sequences.

Details

Calculates the BASELINe posterior probability density function (PDF) for sequences in the provided db. If the db does not contain the required columns to calculate the PDFs (namely OBSERVED & EXPECTED mutations) then the function will:

1. Collapse the sequences by the CLONE column (if present).
2. Calculate the numbers of observed mutations.
3. Calculate the expected frequencies of mutations and modify the provided db. The modified db will be included as part of the returned Baseline object.

The testStatistic indicates the statistical framework used to test for selection. E.g.

• local = CDR_R / (CDR_R + CDR_S).
• focused = CDR_R / (CDR_R + CDR_S + FWR_S).
• imbalance = CDR_R + CDR_S / (CDR_R + CDR_S + FWR_S + FRW_R).

For focused the regionDefinition must only contain two regions. If more than two regions are defined the local test statistic will be used. For further information on the frame of these tests see Uduman et al. (2011).

References

1. Hershberg U, et al. Improved methods for detecting selection by mutation analysis of Ig V region sequences. Int Immunol. 2008 20(5):683-94.
2. Uduman M, et al. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 39(Web Server issue):W499-504.
3. 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 Baseline for the return object. See groupBaseline and summarizeBaseline for further processing. See plotBaselineSummary and plotBaselineDensity for plotting results.

Examples

# Subset example data
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, ISOTYPE %in% c("IgA", "IgG") & SAMPLE == "+7d")
 
# Calculate BASELINe
baseline <- calcBaseline(db, 
                         sequenceColumn="SEQUENCE_IMGT",
                         germlineColumn="GERMLINE_IMGT_D_MASK", 
                         testStatistic="focused",
                         regionDefinition=IMGT_V_NO_CDR3,
                         targetingModel = HS5FModel,
                         nproc=1)