FLIMplots: Functions to plot FLIM results.

In TIMP: Fitting Separable Nonlinear Models in Spectroscopy and Microscopy

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.

Author(s)

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

See Also

fitModel

Examples

## Not run:  
##############################
## 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,
## 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 
##############################

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="")

###############

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)


## End(Not run)

TIMP documentation built on May 2, 2019, 5:55 p.m.