It helps fit the linear quadratic model using the
cellsurvLQfit function of the CFAssay
package, but automates the data wrangling steps, thus making it more
convenient to use for beginner R users. Visit
https://bioconductor.org/packages/release/bioc/html/CFAssay.html for
more details about the method of the fit.
Arguments
- data
A data frame containing at least the following five columns with these exact names: "cline", "Exp", "dose", "ncells", "ncolonies".
- ctype
Name of the cell-line/group for which the model is to be fit.
- method
Method used for the fit. It's
"ml"(maximum likelihood) by default. Also accepts"ls"(least squares) or"franken"(weighted least squares as described by Franken eta al.(2006)).- PEmethod
Controls the value of the plating efficiencies.
"fit"calculates fitted plating efficiencies as model parameters,"fix"uses fixed ones calculated from the observed zero dose data.
Value
An object of class cellsurvLQfit, as returned by
cellsurvLQfit.
Examples
datatab <- CASP8_data
lqmodelFit(datatab, "control-B")
#> ****** Cell type: control-B ******
#>
#> *** Coefficients of LQ-model for cell survival ***
#> method = ml
#> PEmethod = fit
#>
#> Logarithmic plating efficiencies PE fitted as intercepts
#> see remark in the manual, 1.2
#> Estimate Std. Error t value Pr(>|t|)
#> PE1 -1.241244 0.04588891 -27.04888 2.420266e-08
#> PE2 -1.183913 0.04495808 -26.33371 2.914873e-08
#> PE3 -1.252173 0.04607027 -27.17963 2.340599e-08
#>
#> Shape parameters alpha and beta
#> Estimate Std. Error t value Pr(>|t|)
#> alpha -0.01366794 0.034615893 -0.3948458 7.047115e-01
#> beta -0.06047490 0.006936648 -8.7181736 5.243901e-05
#>
#> Observed and fitted plating efficiencies (%):
#> Experiment PE PEfitted
#> PE1 1 29.5 28.9
#> PE2 2 31.0 30.6
#> PE3 3 27.0 28.6
#>
#> Residual Deviance: 6.7948
#> Total residual sum of weighted squares rsswTot: 6.841747
#> Residual Degrees of Freedom: 7
#> Dispersion parameter: 0.9773924
#>
#> Fraction rssw of rsswTot per Experiment
#> Experiment rssw perCent
#> 1 1 0.65 9.5
#> 2 2 0.82 12.0
#> 3 3 5.37 78.5
#>
#> *** Analysis by CellSurvAssay v0.99.0 ***
lqmodelFit(datatab, "control-B", method = "ls", PEmethod = "fix")
#> ****** Cell type: control-B ******
#>
#> *** Coefficients of LQ-model for cell survival ***
#> method = ls
#> PEmethod = fix
#>
#> Shape parameters alpha and beta
#> Estimate Std. Error t value Pr(>|t|)
#> alpha -0.03085093 0.036336074 -0.8490442 4.239383e-01
#> beta -0.05626118 0.006866872 -8.1931296 7.824695e-05
#>
#> Total residual sum of squares rssTot: 0.08600878
#> Residual Degrees of Freedom: 7
#> Multiple R-squared: 0.9985152
#>
#> Fraction rss of rssTot per Experiment
#> Experiment rss perCent
#> 1 1 0.03 31.4
#> 2 2 0.00 5.0
#> 3 3 0.05 63.6
#>
#> *** Analysis by CellSurvAssay v0.99.0 ***