It helps compare two cell survival curves using the
cellsurvLQdiff function of the CFAssay
package, without having the user go through the data wrangling steps
necessary for that. Visit
https://bioconductor.org/packages/release/bioc/html/CFAssay.html for
more details about the method.
Arguments
- data
A data frame containing at least the following five columns: "cline", "Exp", "dose", "ncells", "ncolonies".
- cline1
Name of the cell-line/group that you want to compare.
- cline2
Name of another cell-line/group that you want to compare.
- method
Method used for the fit. It's
"ml"(maximum likelihood) by default. Can be"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 cellsurvLQdiff, as returned by
cellsurvLQdiff.
Examples
datatab <- CASP8_data
compareCurves(datatab, "shCASP8-N", "shCASP8-B+Z+N")
#> ****** Cell type 1: shCASP8-N || Cell type 2: shCASP8-B+Z+N ******
#>
#> Overall comparison test for coefficients alpha and beta of LQ-models
#> ====================================================================
#> method = ml
#> PEmethod = fit
#>
#> 6 PEs fitted as intercepts. To look at, use simple R print function.
#> Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
#> ----------------------------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> alpha -0.01910478 0.020810067 -0.9180547 3.722228e-01
#> beta -0.03692732 0.003798381 -9.7218568 4.061270e-08
#>
#> Goodness-of-fit values
#> Residual Deviance: 12.85619
#> Total sum of squared weighted residuals rsswTot: 13.19573
#> Residual Degrees of Freedom: 16
#> Dispersion parameter: 0.8247333
#>
#> Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
#> -----------------------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> alpha:curvesshCASP8-B+Z+N -0.02224331 0.031717703 -0.7012901 4.946148e-01
#> alpha:curvesshCASP8-N -0.01613085 0.030528750 -0.5283823 6.055094e-01
#> beta:curvesshCASP8-B+Z+N -0.03710309 0.005804313 -6.3923312 1.675728e-05
#> beta:curvesshCASP8-N -0.03678049 0.005559103 -6.6162636 1.158171e-05
#>
#> Goodness-of-fit values
#> Residual Deviance: 12.60644
#> Total sum of squared weighted residuals rsswTot: 12.89855
#> Residual Degrees of Freedom: 14
#> Dispersion parameter: 0.9213253
#>
#> Analysis of Variance Table and F-test
#> Model 2 versus Model 1
#> Resid. Df Resid. Dev Df Deviance F Pr(>F)
#> 1 16 12.856
#> 2 14 12.606 2 0.24975 0.1355 0.8744
#>
#> *** Analysis by CellSurvAssay v0.99.0 ***
compareCurves(datatab, "shCASP8-N", "shCASP8-B+Z+N", method = "ls", PEmethod = "fix")
#> ****** Cell type 1: shCASP8-N || Cell type 2: shCASP8-B+Z+N ******
#>
#> Overall comparison test for coefficients alpha and beta of LQ-models
#> ====================================================================
#> method = ls
#> PEmethod = fix
#>
#> Null hypothesis (Model 1): one set of shape parameters alpha and beta for all data
#> ----------------------------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> alpha -0.01448839 0.019445780 -0.745066 4.670361e-01
#> beta -0.03843262 0.003674907 -10.458122 1.467656e-08
#>
#>
#> Goodness-of-fit values
#> Total sum of squared residuals rssTot: 0.1126081
#> Residual Degrees of Freedom: 16
#> Multiple R-squared: 0.9986025
#>
#> Alternative hypothesis (Model 2): two sets of shape parameters alpha and beta
#> -----------------------------------------------------------------------------
#> Estimate Std. Error t value Pr(>|t|)
#> alpha:curvesshCASP8-B+Z+N -0.020664576 0.02914708 -0.7089759 4.899782e-01
#> alpha:curvesshCASP8-N -0.008312201 0.02914708 -0.2851812 7.796808e-01
#> beta:curvesshCASP8-B+Z+N -0.037657377 0.00550828 -6.8365033 8.107448e-06
#> beta:curvesshCASP8-N -0.039207870 0.00550828 -7.1179873 5.188559e-06
#>
#>
#> Goodness-of-fit values
#> Total sum of squared residuals rssTot: 0.1106845
#> Residual Degrees of Freedom: 14
#> Multiple R-squared: 0.9986266
#>
#> Analysis of Variance Table and F-test
#> Model 2 versus Model 1
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 16 0.11261
#> 2 14 0.11069 2 0.0019235 0.1216 0.8864
#>
#> *** Analysis by CellSurvAssay v0.99.0 ***