summarizeBaseline

summarizeBaseline - Calculate BASELINe summary statistics

Description¶

summarizeBaseline calculates BASELINe statistics such as the mean selection strength (mean Sigma), the 95% confidence intervals and p-values for the presence of selection.

Usage¶

summarizeBaseline(baseline, returnType = c("baseline", "df"), nproc = 1)

Arguments¶

baseline: Baseline object returned by calcBaseline containing annotations and BASELINe posterior probability density functions (PDFs) for each sequence.
returnType: One of c("baseline", "df") defining whether to return a Baseline object (“baseline”) with an updated stats slot or a data.frame (“df”) of summary statistics.
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¶

Either a modified Baseline object or data.frame containing the mean BASELINe selection strength, its 95% confidence intervals, and a p-value for the presence of selection.

Details¶

The returned p-value can be either positive or negative. Its magnitude (without the sign) should be interpreted as per normal. Its sign indicates the direction of the selection detected. A positive p-value indicates positive selection, whereas a negative p-value indicates negative selection.

References¶

Uduman M, et al. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 39(Web Server issue):W499-504.

Examples¶

# Subset example data
data(ExampleDb, package="alakazam")
db <- subset(ExampleDb, c_call == "IGHG")
set.seed(112)
db <- dplyr::slice_sample(db, n=100)

# 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...


# Grouping the PDFs by the sample annotation
grouped <- groupBaseline(baseline, groupBy="sample_id")

Grouping BASELINe probability density functions...
Calculating BASELINe statistics...


# Get a data.frame of the summary statistics
stats <- summarizeBaseline(grouped, returnType="df")

Calculating BASELINe statistics...