transit | R Documentation |
Reimplementation of VanDongen's algorithm for detecting jumps in ion channel recordings.
Deprecation warning: This function is mainly used for patchlamp recordings and may be transferred to a specialised package.
transit(y, x = 1:length(y), x0 = 2 * x[1] - x[2], sigma.amp = NA, sigma.slope = NA,
amp.thresh = 3, slope.thresh = 2, rel.amp.n = 3, rel.amp.thresh = 4,
family = c("gauss", "gaussKern"), param = NULL, refit = FALSE)
y |
a numeric vector containing the serial data |
sigma.amp |
amplitude (i.e. raw data within block) standard deviation; estimated using |
sigma.slope |
slope (i.e. central difference within block) standard deviation; estimated using |
amp.thresh |
amplitude threshold |
slope.thresh |
slope threshold |
rel.amp.n |
relative amplitude threshold will be used for blocks with no more datapoints than this |
rel.amp.thresh |
relative amplitude threshold |
x |
a numeric vector of the same length as |
x0 |
a single numeric giving the last unobserved sample point directly before sampling started |
family , param |
specifies distribution of data, see family |
refit |
should the |
Returns an object of class stepfit
which encodes the jumps and corresponding mean values.
Only central, no forward differences have been used in this implementation. Moreover, the standard deviations will be estimated by sdrobnorm
if omitted (respecting the filter's effect if applicable).
VanDongen, A. M. J. (1996) A new algorithm for idealizing single ion channel data containing multiple unknown conductance levels. Biophysical Journal 70(3), 1303–1315.
stepfit
, sdrobnorm
, jsmurf
, stepbound
, steppath
# estimating step-functions with Gaussian white noise added
# simulate a Gaussian hidden Markov model of length 1000 with 2 states
# with identical transition rates 0.01, and signal-to-noise ratio 2
sim <- contMC(1e3, 0:1, matrix(c(0, 0.01, 0.01, 0), 2), param=1/2)
plot(sim$data, cex = 0.1)
lines(sim$cont, col="red")
# maximum-likelihood estimation under multiresolution constraints
fit.MRC <- smuceR(sim$data$y, sim$data$x)
lines(fit.MRC, col="blue")
# choose number of jumps using BIC
path <- steppath(sim$data$y, sim$data$x, max.blocks=1e2)
fit.BIC <- path[[stepsel.BIC(path)]]
lines(fit.BIC, col="green3", lty = 2)
# estimate after filtering
# simulate filtered ion channel recording with two states
set.seed(9)
# sampling rate 10 kHz
sampling <- 1e4
# tenfold oversampling
over <- 10
# 1 kHz 4-pole Bessel-filter, adjusted for oversampling
cutoff <- 1e3
df.over <- dfilter("bessel", list(pole=4, cutoff=cutoff / sampling / over))
# two states, leaving state 1 at 10 Hz, state 2 at 20 Hz
rates <- rbind(c(0, 10), c(20, 0))
# simulate 0.5 s, level 0 corresponds to state 1, level 1 to state 2
# noise level is 0.3 after filtering
Sim <- contMC(0.5 * sampling, 0:1, rates, sampling=sampling, family="gaussKern",
param = list(df=df.over, over=over, sd=0.3))
plot(Sim$data, pch = ".")
lines(Sim$discr, col = "red")
# fit under multiresolution constraints using filter corresponding to sample rate
df <- dfilter("bessel", list(pole=4, cutoff=cutoff / sampling))
Fit.MRC <- jsmurf(Sim$data$y, Sim$data$x, param=df, r=1e2)
lines(Fit.MRC, col = "blue")
# fit using TRANSIT
Fit.trans <- transit(Sim$data$y, Sim$data$x)
lines(Fit.trans, col = "green3", lty=2)
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