Description Usage Arguments Value Storing of MonteCarlo simulations References See Also Examples
Implements the JUmp Local dEconvolution Segmentation (JULES) filter (Pein et al., 2018). This nonparametric (modelfree) segmentation method combines statistical multiresolution techniques with local deconvolution for idealising patch clamp (ion channel) recordings. In particular, also flickering (events on small time scales) can be detected and idealised which is not possible with common thresholding methods. JULES requires that the underlying noise is homogeneous. If the noise is heterogeneous, hilde
should be used instead. hilde
might be also more powerful (but slower) when events are very short. If all events are very long, multiple times the filter length, the simpler approach jsmurf
is suitable. Further details about how to decide whether the noise is homogeneous or heterogeneous and whether events are short are given in the accompanying vignette.
If q == NULL
a MonteCarlo simulation is required for computing the critical value. Since a MonteCarlo simulation lasts potentially much longer (up to several hours or days if the number of observations is in the millions) than the main calculations, the package saves them by default in the workspace and on the file system such that a second call requiring the same MonteCarlo simulation will be much faster. For more details, in particular to which arguments the MonteCarlo simulations are specific, see Section Storing of MonteCarlo simulations below. Progress of a MonteCarlo simulation can be reported by the argument messages
and the saving can be controlled by the argument option
, both can be specified in ...
and are explained in getCritVal
.
1 2 
data 
a numeric vector containing the recorded data points 
filter 
an object of class 
q 
a single numeric giving the critical value q in (Pein et al., 2018, (7)), by default chosen automatically by 
alpha 
a probability, i.e. a single numeric between 0 and 1, giving the significance level to compute the critical value 
sd 
a single positive numeric giving the standard deviation (noise level) sigma0 of the data points before filtering, by default (NULL) estimated by 
startTime 
a single numeric giving the time at which recording (sampling) of 
output 
a string specifying the return type, see Value 
... 
additional parameters to be passed to

The idealisation (estimation, regression) obtained by JULES. If output == "onlyIdealization"
an object object of class stepblock
containing the idealisation. If output == "eachStep"
a list
containing the entries idealization
with the idealisation, fit
with the fit obtained by the detection step
only, q
with the given / computed critical value, filter
with the given filter and sd
with the given / estimated standard deviation. If output == "everything"
a list
containing the entries idealization
with a list
containing the idealisation after each refining step in the local deconvolution
, fit
with the fit obtained by the detection step
only, stepfit
with the fit obtained by the detection step
before postfiltering, q
with the given / computed critical value, filter
with the given filter and sd
with the given / estimated standard deviation. Additionally, in all cases, the idealisation has an attribute
"noDeconvolution"
, an integer vector, that gives the segments for which no deconvolution could be performed, since two short segments followed each other, see also details in deconvolveLocally
.
If q == NULL
a MonteCarlo simulation is required to compute the critical value. Since a MonteCarlo simulation lasts potentially much longer (up to several hours or days if the number of observations is in the millions) than the main calculations, multiple possibilities for saving and loading the simulations are offered. Progress of a simulation can be reported by the argument messages
which can be specified in ...
and is explained in the documentation of getCritVal
. Each MonteCarlo simulation is specific to the number of observations and the used filter. But note that also MonteCarlo simulations for a (slightly) larger number of observations nq, given in the argument nq
in ...
and explained in the documentation of getCritVal
, can be used, which avoids extensive resimulations for only a little bit varying number of observations, but results in a (small) loss of power. However, simulations of type "vectorIncreased"
, i.e. objects of class "MCSimulationMaximum"
with nq
observations, have to be resimulated if as.integer(log2(n1)) != as.integer(log2(n2))
when the saved simulation was computed with n == n1
and the simulation now is required for n == n2
and nq >= n1
and nq >= n2
. Simulations can either be saved in the workspace in the variable critValStepRTab
or persistently on the file system for which the package R.cache
is used. Moreover, storing in and loading from variables and RDS files is supported. The simulation, saving and loading can be controlled by the argument option
which can be specified in ...
and is explained in the documentation of getCritVal
. By default simulations will be saved in the workspace and on the file system. For more details and for how simulation can be removed see Section Simulating, saving and loading of MonteCarlo simulations in getCritVal
.
Pein, F., TecuapetlaGómez, I., Schütte, O., Steinem, C., Munk, A. (2018) Fullyautomatic multiresolution idealization for filtered ion channel recordings: flickering event detection. IEEE Transactions on NanoBioscience 17(3), 300–320.
getCritVal
, lowpassFilter
, deconvolveLocally
, stepDetection
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156  ## idealisation of the gramicidin A recordings given by gramA with jules
# the used filter
filter < lowpassFilter(type = "bessel", param = list(pole = 4L, cutoff = 1e3 / 1e4),
sr = 1e4)
# idealisation by JULES
# this call requires a MonteCarlo simulation
# and therefore might last a few minutes,
# progress of the MonteCarlo simulation is reported
idealisation < jules(gramA, filter = filter, startTime = 9, messages = 100)
# any second call should be much faster
# as the previous MonteCarlo simulation will be loaded
jules(gramA, filter = filter, startTime = 9)
# much larger significance level alpha for a larger detection power,
# but also with the risk of detecting additional artefacts
# in this example much more changes are detected,
# most of them are probably artefacts, but for instance the event at 11.36972
# might be an additional small event that was missed before
jules(gramA, filter = filter, alpha = 0.9, startTime = 9)
# getCritVal was called in jules, can be called explicitly
# for instance outside of a for loop to save run time
q < getCritVal(length(gramA), filter = filter)
identical(jules(gramA, q = q, filter = filter, startTime = 9), idealisation)
# both steps of JULES can be called separately
fit < stepDetection(gramA, filter = filter, startTime = 9)
identical(deconvolveLocally(fit, data = gramA, filter = filter, startTime = 9),
idealisation)
# more detailed output
each < jules(gramA, filter = filter, startTime = 9, output = "each")
every < jules(gramA, filter = filter, startTime = 9, output = "every")
identical(idealisation, each$idealization)
idealisationEvery < every$idealization[[3]]
attr(idealisationEvery, "noDeconvolution") < attr(every$idealization,
"noDeconvolution")
identical(idealisation, idealisationEvery)
identical(each$fit, fit)
identical(every$fit, fit)
## zoom into a single event, (Pein et al., 2018, Figure 2 lower left panel)
plot(time, gramA, pch = 16, col = "grey30", ylim = c(20, 50),
xlim = c(10.40835, 10.4103), ylab = "Conductance in pS", xlab = "Time in s")
# idealisation
lines(idealisation, col = "red", lwd = 3)
# idealisation convolved with the filter
ind < seq(10.408, 10.411, 1e6)
convolvedSignal < lowpassFilter::getConvolution(ind, idealisation, filter)
lines(ind, convolvedSignal, col = "blue", lwd = 3)
# fit prior to the deconvolution step
# does not fit the recorded data points appropriately
# fit
lines(fit, col = "orange", lwd = 3)
# fit convolved with the filter
ind < seq(10.408, 10.411, 1e6)
convolvedSignal < lowpassFilter::getConvolution(ind, fit, filter)
lines(ind, convolvedSignal, col = "darkgreen", lwd = 3)
## zoom into a single jump
plot(9 + seq(along = gramA) / filter$sr, gramA, pch = 16, col = "grey30",
ylim = c(20, 50), xlim = c(9.6476, 9.6496), ylab = "Conductance in pS",
xlab = "Time in s")
# idealisation
lines(idealisation, col = "red", lwd = 3)
# idealisation convolved with the filter
ind < seq(9.647, 9.65, 1e6)
convolvedSignal < lowpassFilter::getConvolution(ind, idealisation, filter)
lines(ind, convolvedSignal, col = "blue", lwd = 3)
# idealisation with a wrong filter
# does not fit the recorded data points appropriately
wrongFilter < lowpassFilter(type = "bessel",
param = list(pole = 6L, cutoff = 0.2),
sr = 1e4)
# the needed MonteCarlo simulation depends on the number of observations and the filter
# hence a new simulation is required (if called for the first time)
idealisationWrong < jules(gramA, filter = wrongFilter, startTime = 9, messages = 100)
# idealisation
lines(idealisationWrong, col = "orange", lwd = 3)
# idealisation convolved with the filter
ind < seq(9.647, 9.65, 1e6)
convolvedSignal < lowpassFilter::getConvolution(ind, idealisationWrong, filter)
lines(ind, convolvedSignal, col = "darkgreen", lwd = 3)
# simulation for a larger number of observations can be used (nq = 3e4)
# does not require a new simulation as the simulation from above will be used
# (if the previous call was executed first)
jules(gramA[1:2.99e4], filter = filter, startTime = 9,
nq = 3e4, r = 1e3, messages = 100)
# simulation of type "vectorIncreased" for n1 observations can only be reused
# for n2 observations if as.integer(log2(n1)) == as.integer(log2(n2))
# no simulation is required, since a simulation of type "matrixIncreased"
# will be loaded from the fileSystem
# this call also saves a simulation of type "vectorIncreased" in the workspace
jules(gramA[1:1e4], filter = filter, startTime = 9,
nq = 3e4, messages = 100, r = 1e3)
# here a new simulation is required
# (if no appropriate simulation is saved from a call outside of this file)
jules(gramA[1:1e3], filter = filter, startTime = 9,
nq = 3e4, messages = 100, r = 1e3,
options = list(load = list(workspace = c("vector", "vectorIncreased"))))
# the above calls saved and (attempted to) load MonteCarlo simulations
# in the following call the simulations will neither be saved nor loaded
jules(gramA, filter = filter, startTime = 9, messages = 100, r = 1e3,
options = list(load = list(), save = list()))
# only simulations of type "vector" and "vectorInceased" will be saved and
# loaded from the workspace, but no simulations of type "matrix" and
# "matrixIncreased" on the file system
jules(gramA, filter = filter, startTime = 9, messages = 100,
options = list(load = list(workspace = c("vector", "vectorIncreased")),
save = list(workspace = c("vector", "vectorIncreased"))))
# explicit MonteCarlo simulations, not recommended
stat < stepR::monteCarloSimulation(n = length(gramA), , family = "mDependentPS",
filter = filter, output = "maximum",
r = 1e3, messages = 100)
jules(gramA, filter = filter, startTime = 9, stat = stat)
# with given standard deviation
sd < stepR::sdrobnorm(gramA, lag = filter$len + 1)
identical(jules(gramA, filter = filter, startTime = 9, sd = sd), idealisation)
# with less regularisation of the correlation matrix
jules(gramA, filter = filter, startTime = 9, regularization = 0.5)
# with estimation of the level of long segments by the mean
# but requiring 30 observations for it
jules(gramA, filter = filter, startTime = 9,
localEstimate = mean, thresholdLongSegment = 30)
# with one refinement step less, but with a larger grid
# progress of the deconvolution is reported
# potential warning for no deconvolution is suppressed
jules(gramA, filter = filter, startTime = 9,
gridSize = c(1 / filter$sr, 1 / 10 / filter$sr),
windowFactorRefinement = 2, report = TRUE,
suppressWarningNoDeconvolution = TRUE)

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