ratioFitFromDf {CalciOMatic} | R Documentation |
The function ratioFitFromDf
performs a fit on an intracellular
calcium concentration transient obtained from a "fluo_rawdata"
,
after ratiometric transformation. The transient is fitted with a mono-
or a biexponential decay, depending on the value of type
ratioFitFromDf(df, transients = 1, type = "mono",ig = NULL, Plot = FALSE, Fit = TRUE, AfterPeak = FALSE)
df |
a data frame of class "fluo_rawdata" containing all
relevant information (fluorescence transients, background
fluorescence, calibration parameters and exposure times). The
structure of the input data frame must be the same as the one
defined in ratioExpSimul |
transients |
a vector of integers giving the numbers of the transients to fit |
type |
a character string (either "mono" or "bi" ),
specifying the type of calcium exponential decay to consider |
ig |
an object of class "initial_guess" , giving values of
the calcium dynamics parameters to initiate the fitting process with
using nls . This is a list including the following fields:
("log_Ca0" , "log_dCa" , "log_tau" for a
monoexponential decay, eventually "mu" and "log_dtau"
for a biexponential decay). If ig is not an object of class
"initial_guess" , initial guesses are estimated using the
"igRatio" function |
Plot |
a logical value. Set to TRUE to plot the original
signals, the initial guess and the fit results |
Fit |
a logical value. Set to TRUE to perform the fit, or
to FALSE to compute an initial guess only |
AfterPeak |
a logical or numerical value. Set to FALSE to perform the
fit on the whole fluorescence transients, to TRUE to consider
only the part before the fluorescence jump and the convex part after the
fluorescence peak (for both signals), or to an integer to skip a
given number of samples after the fluorescence jump |
The calcium concentration (Ca^2+) is deduced from the
ratiometric transformation (see caFromRatio
). A mono- or
bi-exponential decay is fitted to the Ca^2+ signal,
using the optim
function. For more details, see
ratioFitFromCa
.
An object that inherits from both "nls"
and either
"ratio_fit"
or "ratio_fit_list"
classes, depending
whether transients
is a single value or a vector. In the latter
case, the output "ratio_fit_list"
object is a list of
"ratio_fit"
objects, which have the following attributes:
"Name" |
a character string telling which type of fit has been performed |
"Time" |
the whole time vector (in s) |
"RawData" |
the Ca^2+ signal deduced from the
ratiometric transformation. This signal, which is the one passed to
the nls formula, has two attributes: "var" is the vector
of variances estimated from the error propagation method, and
"Time" is the vector of latencies at which fluorescence
measurements were performed |
"RawDataFrame" |
a copy of the input data frame |
"FitFunction" |
the function passed to the nls formula |
"Subset" |
the indices of the Time vector used for the fit |
Sebastien Joucla sebastien.joucla@parisdescartes.fr
transientConvexPart
,
caFromDf
,
igRatio
,
ratioFitFromCa
## Load the data from cockroach olfactory interneurons data(inVitro) ## Calibrated parameters R_min <- list(value=0.136, mean=0.136, se=0.00363, USE_se=TRUE) R_max <- list(value=2.701, mean=2.701, se=0.151, USE_se=TRUE) K_eff <- list(value=3.637, mean=3.637, se=0.729, USE_se=TRUE) K_d <- list(value=0.583, mean=0.583, se=0.123, USE_se=TRUE) ## Create the data frame containing the physiological data ## (experiment #2, stimulation #2) ## G and s_ro are the respectively the gain of the CCD camera ## and the standard deviation of its read-out process physioData <- ratioExpPhysio(dataset="inVitro", expe=2, stim=2, idxOn=10, R_min=R_min, R_max=R_max, K_eff=K_eff, K_d=K_d, G=0.146, s_ro=16.4, alphamethod=FALSE) ## Perform a ratiometric fit physioRatioFit <- ratioFitFromDf(df = physioData, transients = 2, AfterPeak = 14) ## Print the class of 'physioRatioFit' print(class(physioRatioFit)) ## Create the data frame containing the physiological data ## (experiment #2, stimulation #2) ## G and s_ro are the respectively the gain of the CCD camera ## and the standard deviation of its read-out process physioData <- ratioExpPhysio(dataset="inVitro", expe=2, stim=c(2,3), idxOn=10, R_min=R_min, R_max=R_max, K_eff=K_eff, K_d=K_d, G=0.146, s_ro=16.4, alphamethod=FALSE) ## Perform a ratiometric fit physioRatioFit <- ratioFitFromDf(df = physioData, transients = c(2,3), AfterPeak = 14) ## Print the class of 'physioRatioFit' print(class(physioRatioFit[[2]]))