Stutter ratio estimation
This function tries to estimate the stutter ratio, either in terms of peak heigth ratios or peak surface ratio.
stutterabif(abifdata, chanel, poswild, datapointbefore = 70,
datapointafter = 20, datapointsigma = 3.5,
chanel.names = c(1:4, 105), DATA = paste("DATA", chanel.names[chanel], sep = "."),
maxrfu = 1000, method = "monoH.FC", pms = 6, fig = FALSE)abifdata |
the result returned by |
chanel |
the dye number |
poswild |
the position in datapoint units of the allele at
the origin of the stutter product, typically obtained after a call to |
datapointbefore |
how many datapoints before |
datapointafter |
how many datapoints after |
datapointsigma |
initial guess for the standard deviation of a peak |
chanel.names |
numbers extensions used for the DATA |
DATA |
names of the DATA components |
maxrfu |
argument passed to |
method |
method to be used by |
pms |
how many standard deviations (after gaussian fit) before and after the mean peak values should be considered for spline function interpolation |
fig |
should a summary plot be produced? |
FIXME, See R code for now
A list with the following components:
rh |
Stutter ratio computed as the height of the stutter divided by the height of its corresponding allele |
rs |
Stutter ratio computed as the surface of the stutter divided by the surface of its corresponding allele |
h1 |
The height of the stutter with baseline at 0 |
h2 |
The height of the allele with baseline at 0 |
s1 |
The surface of the stutter |
s2 |
The surface of the allele |
p |
A list of additional parameter that could be usesfull, see example |
J.R. Lobry
# # Load pre-defined dataset, same as what would be obtained with read.abif: # data(JLO) # # Get peak locations in the blue channel: # maxis <- peakabif(JLO, 1, npeak = 6, tmin = 3, fig = FALSE)$maxis # # Compute stutter ratio for first peak and ask for a figure: # tmp <- stutterabif(JLO, 1, maxis[1], fig = TRUE) # # Show in addition the normal approximation used at the stutter peak: # xx <- seq(tmp$p$mu1 - 6*tmp$p$sd1, tmp$p$mu1 + 6*tmp$p$sd1, le = 100) lines(xx, tmp$p$p1*dnorm(xx, tmp$p$mu1, tmp$p$sd1), col = "darkgreen") # # Show in addition the normal approximation used at allele peak: # xx <- seq(tmp$p$mu2 - 6*tmp$p$sd2, tmp$p$mu2 + 6*tmp$p$sd2, le = 100) lines(xx, tmp$p$p2*dnorm(xx, tmp$p$mu2, tmp$p$sd2), col = "darkgreen")
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