shazam (version 0.1.2)

plotBaselineSummary: 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"), style = c("mean"), size = 1, silent = FALSE, ...)

Arguments

baseline

either a data.frame returned from summarizeBaseline or a Baseline object returned from groupBaseline containing selection probability density functions and summary statistics.

idColumn

name of the column in baseline containing primary identifiers. If the input is a Baseline object, then this will be a column in the stats slot of baseline.

groupColumn

name of the column in baseline containing secondary grouping identifiers. If the input is a Baseline object, then this will be a column in the stats slot of baseline.

groupColors

named vector of colors for entries in groupColumn, with names defining unique values in the groupColumn and values being colors. Also controls the order in which groups appear on the plot. If NULL alphabetical ordering and a default color palette will be used. Has no effect if facetBy="group".

subsetRegions

character vector defining a subset of regions to plot, correspoding to the regions for which the baseline data was calculated. If NULL all regions in baseline are plotted.

facetBy

one of c("group", "region") specifying which category to facet the plot by, either values in groupColumn ("group") or regions defined in baseline ("region"). The data that is not used for faceting will be color coded.

style

type of plot to draw. One of:

• "mean": plots the mean and confidence interval for the selection scores of each value in idColumn. Faceting and coloring are determine by values in groupColumn and regions defined in baseline, depending upon the facetBy argument.

size

numeric scaling factor for lines, points and text in the plot.

silent

if TRUE do not draw the plot and just return the ggplot2 object; if FALSE draw the plot.

...

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.

Other selection analysis functions: calcBaseline, groupBaseline, plotBaselineDensity, summarizeBaseline

Examples

# Subset example data
db <- subset(InfluenzaDb, CPRIMER %in% c("IGHA","IGHM") & 
                          BARCODE %in% c("RL016","RL018","RL019","RL021"))

# Calculate BASELINe
# By default, calcBaseline collapses the sequences in the db by the column "CLONE",
# calculates the numbers of observed mutations and expected frequencies of mutations,
# as defined in the IMGT_V_NO_CDR3 and using the HS5FModel targeting model.
# Then, it calculates  the BASELINe posterior probability density functions (PDFs) for
# sequences in the updated db files; using the focused test statistic
db_baseline <- calcBaseline(db, 
                            sequenceColumn="SEQUENCE_IMGT",
                            germlineColumn="GERMLINE_IMGT_D_MASK", 
                            testStatistic="focused",
                            regionDefinition=IMGT_V_NO_CDR3,
                            targetingModel = HS5FModel,
                            nproc = 1)
 
# Grouping the PDFs by the BARCODE and CPRIMER columns in the db, corresponding 
# respectively to sample barcodes and the constant region isotype primers.
baseline <- groupBaseline(db_baseline, groupBy=c("BARCODE", "CPRIMER"))

# Plot mean and confidence interval
plotBaselineSummary(baseline, "BARCODE", "CPRIMER", style="mean")
plotBaselineSummary(baseline, "BARCODE", "CPRIMER", subsetRegions="CDR", style="mean")
plotBaselineSummary(baseline, "BARCODE", "CPRIMER", facetBy="group", style="mean")

# Reorder and recolor groups
group_colors <- c("IGHM"="darkorchid", "IGHA"="steelblue")
plotBaselineSummary(baseline, "BARCODE", "CPRIMER", groupColors=group_colors, style="mean")

# Plot subset of data
stats <- subset(getBaselineStats(baseline), BARCODE %in% c("RL018", "RL019"))
plotBaselineSummary(stats, "BARCODE", "CPRIMER", groupColors=group_colors, style="mean")