calcBaseline

calcBaseline - Calculate the BASELINe PDFs (including for regions that include CDR3 and FWR4)

Description¶

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

Usage¶

calcBaseline(
db,
sequenceColumn = "clonal_sequence",
germlineColumn = "clonal_germline",
testStatistic = c("local", "focused", "imbalanced"),
regionDefinition = NULL,
targetingModel = HH_S5F,
mutationDefinition = NULL,
calcStats = FALSE,
nproc = 1,
cloneColumn = NULL,
juncLengthColumn = NULL
)

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 HH_S5F.
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.
cloneColumn: character name of the column in db containing clonal identifiers. Relevant only for when regionDefinition includes CDR and FWR4 (else this value can be NULL)
juncLengthColumn: character name of the column in db containing the junction length. Relevant only for when regionDefinition includes CDR and FWR4 (else this value can be NULL)

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.

Note: Individual sequences within clonal groups are not, strictly speaking, independent events and it is generally appropriate to only analyze selection pressures on an effective sequence for each clonal group. For this reason, it is strongly recommended that the input db contains one effective sequence per clone. Effective clonal sequences can be obtained by calling the collapseClones function.

If the db does not contain the required columns to calculate the PDFs (namely mu_count & mu_expected) then the function will:

Calculate the numbers of observed mutations.
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).
imbalanced = 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¶

Hershberg U, et al. Improved methods for detecting selection by mutation analysis of Ig V region sequences. Int Immunol. 2008 20(5):683-94.
Uduman M, et al. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 39(Web Server issue):W499-504.
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¶

# Load and subset example data
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call == "IGHG" & sample_id == "+7d")

# Collapse clones
db <- collapseClones(db, cloneColumn="clone_id", 
sequenceColumn="sequence_alignment",
germlineColumn="germline_alignment_d_mask",
method="thresholdedFreq", minimumFrequency=0.6,
includeAmbiguous=FALSE, breakTiesStochastic=FALSE)

# Calculate BASELINe
baseline <- calcBaseline(db, 
sequenceColumn="clonal_sequence",
germlineColumn="clonal_germline", 
testStatistic="focused",
regionDefinition=IMGT_V,
targetingModel=HH_S5F,
nproc=1)

calcBaseline will calculate observed and expected mutations for clonal_sequence using clonal_germline as a reference.

Calculating BASELINe probability density functions...