cfa2way: Two-way analysis for cell survival data
In CFAssay: Statistical analysis for the Colony Formation Assay
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
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
1
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
See Also
glm and family with references for generalized linear modelling.
Examples
1
2
3
4
5
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"))
Example output
[1] "Exp" "x5fuCis" "siRNA" "ncells" "ncolonies"
*** Two-way ANOVA for factors A and B with interaction ***
A= siRNA , B= x5fuCis
Test for interaction: F-test
F Pr(>F)
values 9.831 0.01202 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
*** Logarithmic linear two-way ANOVA for factors A and B with interaction ***
=============================================================================
A= siRNA , B= x5fuCis
Postscript digits for A or B: 0 inactive, 1 active
surv_percent = exp(Estimate)*100
Null hypothesis (Model 1): no interaction
-----------------------------------------
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.4237844 0.06366051 -6.656944 5.660071e-05 65.5
B1 -1.1187559 0.07738539 -14.456939 4.980588e-08 32.7
Goodness-of-fit values
Residual Deviance: 77.99569
Total sum of squared weighted residuals ssqwresTot: 75.87275
Residual Degrees of Freedom: 10
Dispersion parameter: 7.587275
Alternative hypothesis (Model 2): interaction
---------------------------------------------
parametrization: A/B
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.3484218 0.05266374 -6.615972 9.746888e-05 70.6
A0:B1 -0.9757699 0.07170380 -13.608343 2.619891e-07 37.7
A1:B1 -1.3432352 0.09470322 -14.183627 1.832336e-07 26.1
Goodness-of-fit values
Residual Deviance: 37.15596
Total sum of squared weighted residuals ssqwresTot: 37.38767
Residual Degrees of Freedom: 9
Dispersion parameter: 4.154185
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 10 77.996
2 9 37.156 1 40.84 9.831 0.01202 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Two-way ANOVA for factors A and B with interaction ***
A= siRNA , B= x5fuCis
Test for interaction: F-test
F Pr(>F)
values 8.1026 0.0192 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
*** Logarithmic linear two-way ANOVA for factors A and B with interaction ***
=============================================================================
A= A , B= B
Postscript digits for A or B: 0 inactive, 1 active
surv_percent = exp(Estimate)*100
Null hypothesis (Model 1): no interaction
-----------------------------------------
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.4952308 0.08463741 -5.851205 1.613459e-04 60.9
B1 -1.1591090 0.08463741 -13.694997 8.357008e-08 31.4
Goodness-of-fit values
Total sum of squared residuals ssqresTot: 0.2865396
Residual Degrees of Freedom: 10
Multiple R-squared: 0.9925945
Alternative hypothesis (Model 2): interaction
---------------------------------------------
parametrization: A/B
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.3110078 0.09152632 -3.398015 7.898129e-03 73.3
A0:B1 -0.9748860 0.09152632 -10.651428 2.111054e-06 37.7
A1:B1 -1.3433320 0.09152632 -14.677001 1.362680e-07 26.1
Goodness-of-fit values
Total sum of squared residuals ssqresTot: 0.1507872
Residual Degrees of Freedom: 9
Multiple R-squared: 0.996103
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 10 0.28654
2 9 0.15079 1 0.13575 8.1026 0.0192 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
CFAssay documentation built on Nov. 8, 2020, 11:10 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
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
1 | cfa2way(X, A, B, param="A/B", method="ml")
|
Arguments
X |
a data frame which contains columns |
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. |
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
See Also
glm and family with references for generalized linear modelling.
Examples
1 2 3 4 5 | 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"))
|
Example output
[1] "Exp" "x5fuCis" "siRNA" "ncells" "ncolonies"
*** Two-way ANOVA for factors A and B with interaction ***
A= siRNA , B= x5fuCis
Test for interaction: F-test
F Pr(>F)
values 9.831 0.01202 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
*** Logarithmic linear two-way ANOVA for factors A and B with interaction ***
=============================================================================
A= siRNA , B= x5fuCis
Postscript digits for A or B: 0 inactive, 1 active
surv_percent = exp(Estimate)*100
Null hypothesis (Model 1): no interaction
-----------------------------------------
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.4237844 0.06366051 -6.656944 5.660071e-05 65.5
B1 -1.1187559 0.07738539 -14.456939 4.980588e-08 32.7
Goodness-of-fit values
Residual Deviance: 77.99569
Total sum of squared weighted residuals ssqwresTot: 75.87275
Residual Degrees of Freedom: 10
Dispersion parameter: 7.587275
Alternative hypothesis (Model 2): interaction
---------------------------------------------
parametrization: A/B
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.3484218 0.05266374 -6.615972 9.746888e-05 70.6
A0:B1 -0.9757699 0.07170380 -13.608343 2.619891e-07 37.7
A1:B1 -1.3432352 0.09470322 -14.183627 1.832336e-07 26.1
Goodness-of-fit values
Residual Deviance: 37.15596
Total sum of squared weighted residuals ssqwresTot: 37.38767
Residual Degrees of Freedom: 9
Dispersion parameter: 4.154185
Analysis of Variance Table and F-test
Model 2 versus Model 1
Resid. Df Resid. Dev Df Deviance F Pr(>F)
1 10 77.996
2 9 37.156 1 40.84 9.831 0.01202 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
*** Two-way ANOVA for factors A and B with interaction ***
A= siRNA , B= x5fuCis
Test for interaction: F-test
F Pr(>F)
values 8.1026 0.0192 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use 'print' to see detailed results
*** Logarithmic linear two-way ANOVA for factors A and B with interaction ***
=============================================================================
A= A , B= B
Postscript digits for A or B: 0 inactive, 1 active
surv_percent = exp(Estimate)*100
Null hypothesis (Model 1): no interaction
-----------------------------------------
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.4952308 0.08463741 -5.851205 1.613459e-04 60.9
B1 -1.1591090 0.08463741 -13.694997 8.357008e-08 31.4
Goodness-of-fit values
Total sum of squared residuals ssqresTot: 0.2865396
Residual Degrees of Freedom: 10
Multiple R-squared: 0.9925945
Alternative hypothesis (Model 2): interaction
---------------------------------------------
parametrization: A/B
Estimate Std. Error t value Pr(>|t|) surv_percent
A1 -0.3110078 0.09152632 -3.398015 7.898129e-03 73.3
A0:B1 -0.9748860 0.09152632 -10.651428 2.111054e-06 37.7
A1:B1 -1.3433320 0.09152632 -14.677001 1.362680e-07 26.1
Goodness-of-fit values
Total sum of squared residuals ssqresTot: 0.1507872
Residual Degrees of Freedom: 9
Multiple R-squared: 0.996103
Analysis of Variance Table and F-test
Model 2 versus Model 1
Res.Df RSS Df Sum of Sq F Pr(>F)
1 10 0.28654
2 9 0.15079 1 0.13575 8.1026 0.0192 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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