Description Usage Arguments Details Value Author(s) Examples
Plots reported vs. empirical quality scores. Also calculates and outputs Frequency-Weighted Squared Error (FWSE) and reports FWSE on the plot. The points are shaded according to the relative frequency of bases assigned that quality score.
1 2 3 |
ErrRates |
vector of empirical error rates on the Phred scale. |
QualFreq |
vector of relative frequencies of quality scores. |
FWSE_out |
option to output FWSE and report FWSE on the plot. Default = TRUE. |
col |
color of plotted points. Currently allows for only "blue" and "red". Default = "blue". |
max_freq |
Maximum value for color scale, where all quality scores with relative frequency greater than this value are shaded the darkest color. Because the colors are evenly distributed between 0 and max_freq, changing this maximum value may change the color of many points. Default = 0.25. |
lim |
common axis limits for both the x-axis and y-axis. Default = c(0, length(QualFreq) - 1). |
collegend |
include color legend. Default = TRUE. |
xlabel |
x-axis label. Default = "Reported Quality". |
ylabel |
y-axis label. Default = "Empirical Quality". |
main_title |
title. Default = "Reported vs. Empirical Quality". |
FWSEplot plots the reported quality score against the empirical quality score and reports FWSE. If the quality scores accurately reflect the probability of a sequencing error, then the points should fall close to the 45-degree line and FWSE should be close to zero. If the input vectors are $ErrRatesBefore and $QualFreqBefore from the ReQON output, this function will create the bottom left diagnostic plot that is output from ReQON. Similarly, if the input vectors are $ErrRatesAfter and $QualFreqAfter from ReQON output, then the bottom right diagnostic plot is created.
For more details and interpretation, see the vignette by: browseVignettes("ReQON").
In addition to the plot, FWSEplot calculates and outputs Frequency-Weighted Squared Error (FWSE), a measure of how close the points lie to the 45-degree line.
Christopher Cabanski ccabansk@genome.wustl.edu
1 2 3 4 5 6 7 8 9 10 | ## Create relative frequency example data
require( stats )
after <- dnorm( c( 0:40 ), mean = 30, sd = 8 )
after <- after / sum( after )
err_rate <- c( 0:40 ) + rnorm( 41, mean = 0, sd = 5)
err_rate[ which( err_rate < 0 ) ] <- (-1) * err_rate[ which( err_rate < 0 ) ]
## to guarantee that all values are positive
## plot and calculate FWSE
FWSEplot( err_rate, after, col = "red" )
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