FLIMplots: Functions to plot FLIM results.

In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy

Functions to plot FLIM results.

Description

Functions to plot FLIM results.

Usage

    plotHistAmp(multimodel, t, i=1)
    plotHistNormComp(multimodel, t, i=1)
    plotIntenImage(multimodel, t, i=1,  tit=c("Intensity Image"))
    plotSelIntenImage(multimodel, t, i=1, tit=c("Region of Interest"),
     cex=1)
    plotTau(multimodel, t, i=1, tit=" < tau > ", plotoptions=kinopt(),
     lifetimes=TRUE)
    plotNormComp(multimodel, t, i=1)

Arguments

multimodel	the currModel element of the list returned by fitModel
t	the currTheta element of the list returned by fitModel
i	dataset index to make plot for
tit	Character vector giving the title
plotoptions	object of class kinopt giving the plotting options
cex	A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default
lifetimes	A logical value indicating whether the averages per-pixel should be for lifetimes or their inverse, decay rates.

Value

No return value, called for side effects

Author(s)

Katharine M. Mullen, Sergey Laptenok, Ivo H. M. van Stokkum

See Also

fitModel

Examples

##############################
## READ IN DATA,  PREPROCESS DATA
##############################

## data representing only donor tagged

data("donorTagged")

D1 <- preProcess(c001, sel_time=c(25,230))
D2 <- preProcess(c003, sel_time=c(25,230))

## data representing donor-acceptor tagged

data("donorAcceptorTagged")

DA1 <- preProcess(cy005c, sel_time=c(25,230))
DA2 <- preProcess(cy006, sel_time=c(25,230))

##############################
## READ IN MEASURED IRF,  PREPROCESS IRF
##############################

data("mea_IRF")
mea_IRF <- baseIRF(mea_IRF, 100, 150)[25:230]

##############################
## SPECIFY INITIAL MODEL
##############################

modelC <- initModel(mod_type = "kin",
## starting values for decays
kinpar=c(1.52, 0.36),
## numerical convolution algorithm to use
convalg = 2,
## measured IRF
measured_irf = mea_IRF,
lambdac = 650,
## shift of the irf is fixed
parmu = list(0), fixed = list(parmu=1),
## one component represents a pulse-following with the IRF shape
cohspec = list(type = "irf"),
## parallel kinetics
seqmod=FALSE,
## decay parameters are non-negative
positivepar=c("kinpar"),
title="Global CFP bi-exp model with pulse-follower")

##############################
## FIT MODEL FOR DONOR ONLY DATA
##############################

fitD <- fitModel(list(D1,D2),
                 list(modelC),
                 ## estimate the linear coeefficients per-dataset
                 modeldiffs = list(linkclp=list(1,2)),
                 opt=kinopt(iter=1, linrange = 10,
                   addfilename = TRUE,
                   output = "pdf",
                   makeps = "globalD",
                   notraces = TRUE,
                   selectedtraces = seq(1, length(c001@x2), by=11),
                   summaryplotcol = 4, summaryplotrow = 4,
                   ylimspec = c(1, 2.5),
                   xlab = "time (ns)", ylab = "pixel number",
                   FLIM=TRUE))

##############################
## FIT MODEL FOR DONOR-ACCEPTOR DATA
##############################

fitDA <- fitModel(list(DA1,DA2),
                  list(modelC),
                  ## estimate the linear coeefficients per-dataset
                 modeldiffs = list(linkclp=list(1,2)),
                 opt=kinopt(iter=1, linrange = 10,
                   addfilename = TRUE,
                   output = "pdf",
                   makeps = "globalDA",
                   notraces = TRUE,
                   selectedtraces = seq(1, length(c001@x2), by=11),
                   summaryplotcol = 4, summaryplotrow = 4,
                   ylimspec = c(1, 2.5),
                   xlab = "time (ns)", ylab = "pixel number",
                   FLIM=TRUE))

##############################
## COMPARE THE DECAY RATES
##############################

parEst(fitD)

parEst(fitDA)

##############################
## ADDITIONAL FIGURES
##############################
oldpar <- par(no.readonly = TRUE)

par(mfrow=c(2,2), mar=c(1,3,1,12))

par(cex=1.5)
plotIntenImage(fitD$currModel, fitD$currTheta, 1, tit="")

par(cex=1.5)
plotIntenImage(fitDA$currModel, fitD$currTheta, 1, tit="")

par(cex=1.5)
plotIntenImage(fitD$currModel, fitD$currTheta, 2, tit="")

par(cex=1.5)
plotIntenImage(fitDA$currModel, fitD$currTheta, 2, tit="")

par(oldpar)
###############

plo <- kinopt(ylimspec = c(.25,1.1), imagepal=grey(seq(1,0,length=100)))

par(mfrow=c(2,2), mar=c(1,3,1,12))

par(cex=1.5)
plotTau(fitD$currModel, fitD$currTheta, 1, tit="",plotoptions=plo,
        lifetimes=FALSE)

par(cex=1.5)
plotTau(fitDA$currModel, fitD$currTheta, 1, tit="",plotoptions=plo,
        lifetimes=FALSE)

par(cex=1.5)
plotTau(fitD$currModel, fitD$currTheta, 2, tit="",plotoptions=plo,
        lifetimes=FALSE)

par(cex=1.5)
plotTau(fitDA$currModel, fitD$currTheta, 2, tit="", plotoptions=plo,
        lifetimes=FALSE)

par(oldpar)
 # end donttest

##############################
## CLEANUP GENERATED FILES
##############################
# This removes the files that were generated in this example
# (do not run this code if you wish to inspect the output)
file_list_cleanup = c('globalDA_paramEst.txt', 'globalDA_spec_dataset_1.txt',
  'globalDA_spec_dataset_2.txt', 'globalD_paramEst.txt',
  'globalD_spec_dataset_1.txt', 'globalD_spec_dataset_2.txt',
  Sys.glob("*paramEst.txt"), Sys.glob("*.ps"), Sys.glob("Rplots*.pdf"))

# Iterate over the files and delete them if they exist
for (f in file_list_cleanup) {
  if (file.exists(f)) {
    unlink(f)
  }
}

TIMP documentation built on Dec. 28, 2022, 3:06 a.m.