Package 'CFAssay' reference manual

Title:	Statistical analysis for the Colony Formation Assay
Description:	The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay.
Authors:	Herbert Braselmann
Maintainer:	Herbert Braselmann <[email protected]>
License:	LGPL
Version:	1.41.0
Built:	2024-11-18 04:38:31 UTC
Source:	https://github.com/bioc/CFAssay

Comparison of two linear-quadratic cell survival curves

Description

The function does an ANOVA test for overall comparison of the parameters $alpha$ and $beta$ of two linear-quadratic cell survival curves. The parameters are fitted simultaneously to the data with this function, i.e. no other function is necessary to derive the fits.

Usage

cellsurvLQdiff(X, curvevar, method="ml", PEmethod="fit")
cellsurvLQdiff(X, curvevar, method="ml", PEmethod="fit")

Arguments

`X`	A data frame which contains columns `Exp, dose, ncells, ncolonies` and a further column containing two different values (character strings), which identify the two curves. Moreover, if there is no 0-value in the `dose`-column, `X` has to contain a column `pe` for plating efficiencies.
`curvevar`	Character string, which has to be one of the column names of the data frame `X`, that contains the two different values (character strings that distinguishes between the two curves).
`method`	Determines the method used for the fit. `"ml"` is for maximum-likelihood, `"ls"` for least-squares. `"franken"` performs weigthed least-squares with weights as described in Franken et al. (2006).
`PEmethod`	Controls the value of the plating efficiencies, i.e. the colony counts for untreated cells. `"fit"` calculates fitted plating efficiencies as model parameters, `"fix"` uses fixed ones calculated from the observed zero dose data or from a column named `pe` in `X`.

Details

In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. method="ml" for maximum-likelihood uses R function glm with family "quasipoisson" and link function "log". method="ls" uses R function lm.

Value

The function returns an object of class cellsurvLQdiff containing three elements, fit1, fit2 and anv. fit1 and fit2 are objects of class glm when method="ml" or of class lm when method="ls". fit1 has parameters alpha and beta fitted in common for both cell survival curves. fit2 has parameters alpha and beta fitted differently for both curves. anv is of class anova and contains the F-test. Test results are printed, however, the full result inlcuding curve parameters is returned invisibly, i.e. the function has to be used with print or assigned to a variable, say for e.g. fitcomp as in the example below.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab) #contains a column "cline"
table(datatab$cline)
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
plot(cellsurvLQfit(subset(datatab, cline=="okf6TERT1")), col=1)
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)
legend(0, 0.02, c("okf6TERT1", "cal33"), text.col=1:2)
#using different options:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab) #contains a column "cline"
table(datatab$cline)
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
plot(cellsurvLQfit(subset(datatab, cline=="okf6TERT1")), col=1)
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)
legend(0, 0.02, c("okf6TERT1", "cal33"), text.col=1:2)
#using different options:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))

Fit the linear-quadratic (LQ) model to cell survival data

Description

This function calculates the linear coefficient $alpha$ and the coefficient $beta$ of the dose-squared term (see manual for this R-package) for colony counts measured for a set of irradiation doses and repeated experiments. The function is a wrapper for the R-functions glm or lm, which simplifies use of these functions for cell survival data.

Usage

cellsurvLQfit(X, method="ml", PEmethod="fit")
cellsurvLQfit(X, method="ml", PEmethod="fit")

Arguments

`X`	A data frame which contains at least columns `Exp, dose, ncells, ncolonies` and if there is no 0-value in the `dose`-column, `X` has to contain a further column `pe` for plating efficiencies.
`method`	Determines the method used for the fit. `"ml"` is for maximum-likelihood, `"ls"` for least-squares. `"franken"` performs weigthed least-squares with weights as described in Franken et al. (2006).
`PEmethod`	Controls the value of the plating efficiencies, i.e. the colony counts for untreated cells. `"fit"` calculates fitted plating efficiencies as model parameters, `"fix"` uses fixed ones calculated from the observed zero dose data or from a column named `pe` in `X`.

Details

In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. method="ml" uses R function glm with quasipoisson family and link function "log". method="ls" uses R function lm. PEmethod="fit" fits plating efficiencies for every experiments. PEmethod="fix" uses observed plating efficiencies. If there is no 0-value in the dose-column, PEmethod is overwritten with "fix" and X has to contain a further column pe containing the plating efficiencies, i.e. ncolonies/ncells from untreated cells, not per hundred or percent.

Value

The function returns an object of class cellsurvLQfit, which is similar to classes glm or lm, however containing two additional entries, type and PEmethod, which are used for printing and plotting. The full result is returned invisibly, i.e. the function has to be used with print or plot or assigned to a variable, say for e.g. fit as in the example below.

Author(s)

Herbert Braselmann

References

Franken NAP, Rodermond HM, Stap J, et al. Clonogenic assay of cells in vitro. Nature Protoc 2006;1:2315-19.

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X) #using default options
print(fit)
print(fit$type)
print(fit$PEmethod)
#Using other options:
print(cellsurvLQfit(X, method="ls"))
print(cellsurvLQfit(X, PEmethod="fix"))
print(cellsurvLQfit(X, method="ls", PEmethod="fix"))
print(cellsurvLQfit(X, method="franken"))
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X) #using default options
print(fit)
print(fit$type)
print(fit$PEmethod)
#Using other options:
print(cellsurvLQfit(X, method="ls"))
print(cellsurvLQfit(X, PEmethod="fix"))
print(cellsurvLQfit(X, method="ls", PEmethod="fix"))
print(cellsurvLQfit(X, method="franken"))

Two-way analysis for cell survival data

Description

The function does an ANOVA of cell survival data from experimental 2-way designs where a treatment factor is tested on a control and on an altered cell line or where two different simultaneous treatments are tested on cells from a common unaltered clone. The function is a wrapper for the R-function glm. quasipoisson family is used with link function "log", i.e. dependency of treatment factors is considered as logarithmically additive.

Usage

cfa2way(X, A, B, param="A/B", method="ml")
cfa2way(X, A, B, param="A/B", method="ml")

Arguments

`X`	a data frame which contains columns `Exp, ncells, ncolonies` and two further columns for the treatment variables, see details.
`A`	a character string containing the name of a treatment or cell line variable (first factor in the model)
`B`	a character string containing the name of a treatment or cell line variable (second factor in the model)
`param`	Controls the parametrization of the model. Options are "A/B" for B nested in A , "B/A" for A nested in B and "A*B" for interaction term.
`method`	determines the method used for the fit. `"ml"` is for maximum-likelihood, `"ls"` for least-squares.

Details

In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. The two treatment or cell line columns should have numeric values 0, 1, ... for 2, 3, ... levels. For e.g. if a column describes clonal alteration (transfection, knock-down etc.) by a gene then 0 means unaltered or control and 1 means altered. Similar if a column describes treatment with one dose then 0 means untreated and 1 treated. 2 would indicate another dose level from the same treatment drug without taking it as a continuous covariate as for cell survival curves for radiation.

Value

The function returns an object of class cfa2way containing three elements, fit1, fit2 and anv. fit1 and fit2 are objects of class glm when method="ml" or of class lm when method="ls". fit1 has logarithmic additive parameters without interaction. fit2 has logarithmic additive parameters and interaction. anv is of class anova and contains the F-test. The full result is returned invisibly, i.e. the function has to be used with print or assigned to a variable, say for e.g. fitcomp as in the example below.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))
print(cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B", method="ls"))
datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))
print(cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B", method="ls"))

Calculation of plating efficiencies from a curve data set containing one specified curve

Description

The function calculates plating efficiencies, i.e. fractions of colonies per cell of untreated cells, for every experimental replicate in a data frame with one specified curve. For that, lines with zero dose ( $dose = 0$ ) are extracted from the data frame.

Usage

pes(X)
pes(X)

Arguments

`X`	A data frame which contains columns `Exp, dose, ncells, ncolonies`.

Details

In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric.

Value

The function returns a data frame with three columns Exp, pe and S0, containing experiment identifiers (biological replicates), measured plating efficiencies and plating efficiencies fitted separately for each repeated experiments. Rows of the data frame are named for the different experiments.

Author(s)

Herbert Braselmann

Examples

datatab <- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X <- subset(datatab, cline=="okf6TERT1")  #Specification of curve
pes(X)
pes(subset(datatab, cline=="cal33") )
datatab <- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X <- subset(datatab, cline=="okf6TERT1")  #Specification of curve
pes(X)
pes(subset(datatab, cline=="cal33") )

Plot of an LQ model fit

Description

This function plots a cell survival curve derived from fitting an LQ model with function cellsurvLQfit

Usage

## S3 method for class 'cellsurvLQfit'
plot(x, xlim = NULL, ylim = c(0.008, 1), xlab = "Dose (Gy)", ylab = "Survival (1 = 100%)",col=1, pch=1, add=FALSE, ...)
## S3 method for class 'cellsurvLQfit'
plot(x, xlim = NULL, ylim = c(0.008, 1), xlab = "Dose (Gy)", ylab = "Survival (1 = 100%)",col=1, pch=1, add=FALSE, ...)

Arguments

`x`	an object of class `cellsurvLQfit` resulting from function `cellsurvLQfit`.
`xlim`	plot range for the x-axis. Default is the dose range of the data.
`ylim`	plot range for the y-axis. Default is from 0.008 to 1.0
`xlab`	label for the x-axis. Default is "Dose (Gy)".
`ylab`	label for the y-axis. Default is "Survival (1 = 100%)".
`col`	colour for plot. Default is `col = 1`.
`pch`	symbol for plotting points. Default is `pch = 1`.
`add`	logical; if TRUE add to an already existing plot, see `curve`.
`...`	further arguments to pass to R function `plot`.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
plot(fit)
S0 <- pes(X)$S0
names(S0) <- pes(X)$Exp
sfpmean(X, S0) #values of plotted mean survival fractions and error bars 
# add second plot
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
plot(fit)
S0 <- pes(X)$S0
names(S0) <- pes(X)$Exp
sfpmean(X, S0) #values of plotted mean survival fractions and error bars 
# add second plot
plot(cellsurvLQfit(subset(datatab, cline=="cal33")), col=2, add=TRUE)

Generic plotting of experimental repeats

Description

Generic plotting of experimental repeats of cell survival data in separated plots. plotExp methods are defined for objects resulting from CFAssay functions cellsurvLQfit and cfa2way.

Usage

plotExp(x, ...)
plotExp(x, ...)

Arguments

`x`	should be an object of class `cellsurvLQfit` or `cfa2way`, see details.
`...`	further arguments to pass to generic function `plotExp`, dependent of the class of the argument `x`.

Details

For other objects than of class cellsurvLQfit or cfa2way, plot.default will be called. In this version of CFAssay this will give an error message and a hint to use one of the two defined classes.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()
## Not run: 
x <- 1
plotExp(X) #yields an error for this data type

## End(Not run)
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()
## Not run: 
x <- 1
plotExp(X) #yields an error for this data type

## End(Not run)

Diagnostic plots of LQ model versus experiments

Description

This function plots the fit of an LQ model versus the fits of each experiment in a series of plots. It is recommended to direct it in a pdf-file.

Usage

## S3 method for class 'cellsurvLQfit'
plotExp(x, xlim = NULL, ylim = c(0.001, 1.5), xlab = "Dose (Gy)", ylab = "Survival (1 = 100%)", ...)
## S3 method for class 'cellsurvLQfit'
plotExp(x, xlim = NULL, ylim = c(0.001, 1.5), xlab = "Dose (Gy)", ylab = "Survival (1 = 100%)", ...)

Arguments

`x`	an object of class `cellsurvLQfit` resulting from function `cellsurvLQfit`.
`xlim`	range of x axis (dose).
`ylim`	range of y-axis.
`xlab`	label of x-axis.
`ylab`	label of y-axis.
`...`	further arguments to pass to generic function `plotExp`.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
print(fit)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
print(fit)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()

Diagnostic plot of experimental replicates for two-way analysis of cell survival data

Description

This function plots the estimated means of a two-way ANOVA for each experiment in a series of plots. It is recommended to direct it in a pdf-file.

Usage

## S3 method for class 'cfa2way'
plotExp(x, labels=c(A="A",B="B"), ...)
## S3 method for class 'cfa2way'
plotExp(x, labels=c(A="A",B="B"), ...)

Arguments

`x`	an R object of class `cfa2way` and resulting from function `cfa2way`.
`labels`	Labels for output description. These describe the meaning of `A` and of `B`.
`...`	further arguments to pass to generic function `plotExp`.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))
  pdf("TwoWay_experimental_plots.pdf")
    plotExp(fitcomp, labels=c(A="siRNA", B="x5fuCis"))
  dev.off()
datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))
  pdf("TwoWay_experimental_plots.pdf")
    plotExp(fitcomp, labels=c(A="siRNA", B="x5fuCis"))
  dev.off()

Default function for plotting of experimental repeats

Description

plotExp.default is the formal default method of the generic plotExp function.

Usage

## Default S3 method:
plotExp(x, ...)
## Default S3 method:
plotExp(x, ...)

Arguments

`x`	should be an object of class `cellsurvLQfit` or `cfa2way`, see details.
`...`	further arguments to pass to generic function `plotExp`, dependent of the class of the argument `x`.

Details

In this version of CFAssay other objects than of class cellsurvLQfit or cfa2way this will give an error message and a hint to use one of the two defined classes.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()
## Not run: 
x <- 1
plotExp(X) #yields an error for this data type

## End(Not run)
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X)
#pdf("okf6TERT1_experimental_plots.pdf")
   plotExp(fit)
#dev.off()
## Not run: 
x <- 1
plotExp(X) #yields an error for this data type

## End(Not run)

Print test results of comparison of two linear-quadratic cell survival curves

Description

The function prints linear coefficients alpha and dose-squared coefficients beta of calculated with function cellsurvLQdiff. In addition quality statistics of the fit and ANOVA F-test results for overall comparison of the coefficients are printed.

Usage

## S3 method for class 'cellsurvLQdiff'
print(x, ...)
## S3 method for class 'cellsurvLQdiff'
print(x, ...)

Arguments

`x`	an object of class `cellsurvLQdiff` resulting from function `cellsurvLQdiff`.
`...`	further arguments to pass to R function `print`.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
#using different options for cellsurvLQdiff:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
fitcomp<- cellsurvLQdiff(datatab, curvevar="cline") #using default options
print(fitcomp)
#using different options for cellsurvLQdiff:
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls"))
print(cellsurvLQdiff(datatab, curvevar="cline", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="ls", PEmethod="fix"))
print(cellsurvLQdiff(datatab, curvevar="cline", method="franken"))

Print summary of an LQ-model fit for cell survival data

Description

The function prints the results of an LQ-model fit for radiation dose dependent cell survival.

Usage

## S3 method for class 'cellsurvLQfit'
print(x, ...)
## S3 method for class 'cellsurvLQfit'
print(x, ...)

Arguments

`x`	an object of class `cellsurvLQfit` resulting from `cellsurvLQfit`.
`...`	further arguments to pass to R function `print`.

Details

In this version of CFAssay the class argument x is checked by its entry fit$type and results in an error, when x results from an independent use of glm or lm.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X) #using default options
print(fit)
print(fit$type)
print(fit$PEmethod)
#using other options
print(cellsurvLQfit(X, method="ls"))
print(cellsurvLQfit(X, PEmethod="fix"))
print(cellsurvLQfit(X, method="ls", PEmethod="fix"))
print(cellsurvLQfit(X, method="franken"))
datatab<- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X<- subset(datatab, cline=="okf6TERT1")
fit<- cellsurvLQfit(X) #using default options
print(fit)
print(fit$type)
print(fit$PEmethod)
#using other options
print(cellsurvLQfit(X, method="ls"))
print(cellsurvLQfit(X, PEmethod="fix"))
print(cellsurvLQfit(X, method="ls", PEmethod="fix"))
print(cellsurvLQfit(X, method="franken"))

Print summary of two-way analysis for cell survival data.

Description

The function prints a summary of two-way analysis for cell survival data.

Usage

## S3 method for class 'cfa2way'
print(x, labels=c(A="A",B="B"), ...)
## S3 method for class 'cfa2way'
print(x, labels=c(A="A",B="B"), ...)

Arguments

`x`	an R object of class `cfa2way` and resulting from function `cfa2way`.
`labels`	labels for output description. These describe the meaning of `A` and of `B`.
`...`	further arguments to pass to R function `print`.

Author(s)

Herbert Braselmann

Examples

datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))
datatab<- read.table(system.file("doc", "exp2_2waycfa.txt", package="CFAssay"), header=TRUE, sep="\t")
names(datatab)  # has columns "x5fuCis" and "siRNA" 
fitcomp<- cfa2way(datatab, A="siRNA", B="x5fuCis", param="A/B")
print(fitcomp, labels=c(A="siRNA",B="x5fuCis"))

Pointwise mean survival fractions for curves with several experimental replicates

Description

The function calculates mean survival fractions for curves averaged over experimental replicates. The function is employed by function plot.cellsurvLQfit for plotting observed means

Usage

sfpmean(X, S0=NULL)
sfpmean(X, S0=NULL)

Arguments

`X`	A data frame which contains columns `Exp, dose, ncells, ncolonies` and if `S0=NULL`, `X` has to contain a further column `pe` for plating efficiencies.
`S0`	If not `NULL`, a named numerical vector of length equal to the number of different experiments, i.e. `length(S0)==length(unique(X$Exp))` has to be `TRUE`. Default is `S0=NULL`, i.e. undefined.

Details

In the data frame X, Exp identifies the experimental replicates and may be numeric or non-numeric. S0 may contain plating efficiencies for each replicate, resulting from function pes or from cellsurvLQfit (fitted). When S0=NULL, X must have a column with name pe, containing the plating efficiencies.

Value

A numerical matrix with two rows, the first row containing the survival fractions for each radiation dose, second row the standard deviations.

Author(s)

Herbert Braselmann

Examples

datatab <- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X <- subset(datatab, cline=="okf6TERT1")
## S0 from data with fucntion pes
S0 <- pes(X)$pe  #observed plating efficiencies
length(S0)==length(unique(X$Exp)) #length ok?
names(S0) <- pes(X)$Exp
sfpmean(X, S0)
## Not run: 
## S0 from LQ model fit
fit <- cellsurvLQfit(X)
fit$coef  #contains fitted log-pe
grep("Exp",names(fit$coef))
S01 <- exp(fit$coef[1:8])  #fitted pe
sfpmean(X, S01) #names of S01 still to adjust!

## End(Not run)
## Not run: sfpmean(X)  #yields an error for this data set
datatab <- read.table(system.file("doc", "expl1_cellsurvcurves.txt", package="CFAssay"), header=TRUE, sep="\t")
X <- subset(datatab, cline=="okf6TERT1")
## S0 from data with fucntion pes
S0 <- pes(X)$pe  #observed plating efficiencies
length(S0)==length(unique(X$Exp)) #length ok?
names(S0) <- pes(X)$Exp
sfpmean(X, S0)
## Not run: 
## S0 from LQ model fit
fit <- cellsurvLQfit(X)
fit$coef  #contains fitted log-pe
grep("Exp",names(fit$coef))
S01 <- exp(fit$coef[1:8])  #fitted pe
sfpmean(X, S01) #names of S01 still to adjust!

## End(Not run)
## Not run: sfpmean(X)  #yields an error for this data set

Package 'CFAssay'

Help Index

Comparison of two linear-quadratic cell survival curves

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Fit the linear-quadratic (LQ) model to cell survival data

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Two-way analysis for cell survival data

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Calculation of plating efficiencies from a curve data set containing one specified curve

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Plot of an LQ model fit

Description

Usage

Arguments

Author(s)

See Also

Examples

Generic plotting of experimental repeats

Description

Usage

Arguments

Details

Author(s)

See Also

Examples

Diagnostic plots of LQ model versus experiments

Description

Usage

Arguments

Author(s)

See Also

Examples

Diagnostic plot of experimental replicates for two-way analysis of cell survival data

Description

Usage

Arguments

Author(s)

See Also

Examples

Default function for plotting of experimental repeats

Description

Usage

Arguments

Details

Author(s)

See Also

Examples

Print test results of comparison of two linear-quadratic cell survival curves

Description

Usage

Arguments

Author(s)