shazam (version 0.1.8)

slideWindowTunePlot: Visualize parameter tuning for sliding window approach

Description

Visualize results from slideWindowTune

Usage

slideWindowTunePlot(tuneList, plotFiltered = TRUE, percentage = FALSE,
  jitter.x = FALSE, jitter.x.amt = 0.1, jitter.y = FALSE,
  jitter.y.amt = 0.1, pchs = 1, ltys = 2, cols = 1, plotLegend = TRUE,
  legendPos = "topright", legendHoriz = FALSE, legendCex = 1,
  title = NULL)

Arguments

tuneList

a list of logical matrices returned by slideWindowTune.

plotFiltered

whether to plot the number of filtered sequences (as opposed to the number of remaining sequences). Default is TRUE.

percentage

whether to plot on the y-axis the percentage of filtered sequences (as opposed to the absolute number). Default is FALSE.

jitter.x

whether to jitter x-axis values. Default is FALSE.

jitter.x.amt

amount of jittering to be applied on x-axis values if jitter.x=TRUE. Default is 0.1.

jitter.y

whether to jitter y-axis values. Default is FALSE.

jitter.y.amt

amount of jittering to be applied on y-axis values if jitter.y=TRUE. Default is 0.1.

pchs

point types to pass on to plot.

ltys

line types to pass on to plot.

cols

colors to pass on to plot.

plotLegend

whether to plot legend. Default is TRUE.

legendPos

position of legend to pass on to legend. Can be either a numeric vector specifying x-y coordinates, or one of "topright", "center", etc. Default is "topright".

legendHoriz

whether to make legend horizontal. Default is FALSE.

legendCex

numeric values by which legend should be magnified relative to 1.

title

plot main title. Default is NULL (no title)

Details

For each windowSize, the numbers of sequences filtered or remaining after applying the sliding window approach are plotted on the y-axis against thresholds on the number of mutations in a window on the x-axis.

When plotting, a user-defined amount of jittering can be applied on values plotted on either axis or both axes via adjusting jitter.x, jitter.y, jitter.x.amt and jitter.y.amt. This may be help with visually distinguishing lines for different window sizes in case they are very close or identical to each other. If plotting percentages (percentage=TRUE) and using jittering on the y-axis values (jitter.y=TRUE), it is strongly recommended that jitter.y.amt be set very small (e.g. 0.01).

NA for a combination of mutThresh and windowSize where mutThresh is greater than windowSize will not be plotted.

See Also

See slideWindowTune for how to get tuneList. See jitter for use of amount of jittering.

Examples

# NOT RUN {
# Use an entry in the example data for input and germline sequence
data(ExampleDb, package="alakazam")

# Try out thresholds of 2-4 mutations in window sizes of 3-5 nucleotides 
# on a subset of ExampleDb
tuneList <- slideWindowTune(db = ExampleDb[1:10, ], 
                           mutThreshRange = 2:4, windowSizeRange = 3:5,
                           verbose = FALSE)

# Visualize
# Plot numbers of sequences filtered without jittering y-axis values
slideWindowTunePlot(tuneList, pchs=1:3, ltys=1:3, cols=1:3, 
                    plotFiltered=TRUE, jitter.y=FALSE)
                    
# Notice that some of the lines overlap
# Jittering could help
slideWindowTunePlot(tuneList, pchs=1:3, ltys=1:3, cols=1:3,
                    plotFiltered=TRUE, jitter.y=TRUE)
                    
# Plot numbers of sequences remaining instead of filtered
slideWindowTunePlot(tuneList, pchs=1:3, ltys=1:3, cols=1:3, 
                    plotFiltered=FALSE, jitter.y=TRUE, 
                    legendPos="bottomright")
                    
# Plot percentages of sequences filtered with a tiny amount of jittering
slideWindowTunePlot(tuneList, pchs=1:3, ltys=1:3, cols=1:3,
                    plotFiltered=TRUE, percentage=TRUE, 
                    jitter.y=TRUE, jitter.y.amt=0.01)
                                                            
# }