groupBaseline

groupBaseline - Group BASELINe PDFs

Description¶

groupBaseline convolves groups of BASELINe posterior probability density functions (PDFs) to get combined PDFs for each group.

Usage¶

groupBaseline(baseline, groupBy, nproc = 1)

Arguments¶

baseline: Baseline object containing the db and the BASELINe posterior probability density functions (PDF) for each of the sequences, as returned by calcBaseline.
groupBy: The columns in the db slot of the Baseline object by which to group the sequence PDFs.
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 the BASELINe posterior probability density functions (PDF) for each of the groups.

Details¶

While the selection strengths predicted by BASELINe perform well on average, the estimates for individual sequences can be highly variable, especially when the number of mutations is small.

To overcome this, PDFs from sequences grouped by biological or experimental relevance, are convolved to from a single PDF for the selection strength. For example, sequences from each sample may be combined together, allowing you to compare selection across samples. This is accomplished through a fast numerical convolution technique.

References¶

Yaari G, et al. Quantifying selection in high-throughput immunoglobulin sequencing data sets. Nucleic Acids Res. 2012 40(17):e134. (Corrections at http://selection.med.yale.edu/baseline/correction/)

Examples¶

### Not run:
# Subset example data from alakazam as a demo
# data(ExampleDb, package="alakazam")
# db <- subset(ExampleDb, c_call %in% c("IGHM", "IGHG"))
# set.seed(112)
# db <- dplyr::slice_sample(db, n=200)
# 
# # 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)
# 
# # Group PDFs by sample
# grouped1 <- groupBaseline(baseline, groupBy="sample_id")
# sample_colors <- c("-1h"="steelblue", "+7d"="firebrick")
# plotBaselineDensity(grouped1, idColumn="sample_id", colorValues=sample_colors, 
# sigmaLimits=c(-1, 1))
#  
# # Group PDFs by both sample (between variable) and isotype (within variable)
# grouped2 <- groupBaseline(baseline, groupBy=c("sample_id", "c_call"))
# isotype_colors <- c("IGHM"="darkorchid", "IGHD"="firebrick", 
# "IGHG"="seagreen", "IGHA"="steelblue")
# plotBaselineDensity(grouped2, idColumn="sample_id", groupColumn="c_call",
# colorElement="group", colorValues=isotype_colors,
# sigmaLimits=c(-1, 1))
# # Collapse previous isotype (within variable) grouped PDFs into sample PDFs
# grouped3 <- groupBaseline(grouped2, groupBy="sample_id")
# sample_colors <- c("-1h"="steelblue", "+7d"="firebrick")
# plotBaselineDensity(grouped3, idColumn="sample_id", colorValues=sample_colors,
# sigmaLimits=c(-1, 1))