| plotBaselineSummary {shazam} | R Documentation | 
Plots BASELINe summary statistics
Description
plotBaselineSummary plots a summary of the results of selection analysis 
using the BASELINe method.
Usage
plotBaselineSummary(
  baseline,
  idColumn,
  groupColumn = NULL,
  groupColors = NULL,
  subsetRegions = NULL,
  facetBy = c("region", "group"),
  title = NULL,
  style = c("summary"),
  size = 1,
  silent = FALSE,
  ...
)
Arguments
baseline | 
 either a data.frame returned from summarizeBaseline
or a   | 
idColumn | 
 name of the column in   | 
groupColumn | 
 name of the column in   | 
groupColors | 
 named vector of colors for entries in   | 
subsetRegions | 
 character vector defining a subset of regions to plot, correspoding 
to the regions for which the   | 
facetBy | 
 one of c("group", "region") specifying which category to facet the
plot by, either values in   | 
title | 
 string defining the plot title.  | 
style | 
 type of plot to draw. One of: 
  | 
size | 
 numeric scaling factor for lines, points and text in the plot.  | 
silent | 
 if   | 
... | 
 additional arguments to pass to ggplot2::theme.  | 
Value
A ggplot object defining the plot.
See Also
Takes as input either a Baseline object returned by groupBaseline or a data.frame returned from summarizeBaseline.
Examples
# Subset example data 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=25)
# 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)
 
# Grouping the PDFs by sample and isotype annotations
grouped <- groupBaseline(baseline, groupBy=c("sample_id", "c_call"))
# Plot mean and confidence interval by region with custom group colors
isotype_colors <- c("IGHM"="darkorchid", "IGHD"="firebrick", 
                    "IGHG"="seagreen", "IGHA"="steelblue")
plotBaselineSummary(grouped, "sample_id", "c_call", 
                    groupColors=isotype_colors, facetBy="region")