The test for comparing counts from two or more digital PCR experiments.
test_counts(input, model = "ratio", conf.level = 0.95)
adpcr or dpcr
with "nm" type.binomial, poisson,
prop, ratio. See Details.an object of class count_test.
test_counts incorporates two different approaches to models: GLM (General Linear
Model) and multiple pair-wise tests. The GLM fits counts data from different
digital PCR experiments using quasibinomial or quasipoisson family.
Comparisons between single experiments utilize Tukey's contrast and multiple t-tests
(as provided by function glht).
In case of pair-wise tests, (rateratio.test or
prop.test) are used to compare all pairs of experiments. The
p-values are adjusted using the Benjamini & Hochberg method (p.adjust).
Furthermore, confidence intervals are simultaneous.
Mean number of template molecules per partition and its confidence intervals will vary depending on input.
Bretz F, Hothorn T, Westfall P, Multiple comparisons using R. Boca Raton, Florida, USA: Chapman & Hall/CRC Press (2010).
Functions used by test_counts:
GUI presenting capabilities of the test: test_counts_gui.
#be warned, the examples of test_counts are time-consuming ## Not run: ------------------------------------ # adpcr1 <- sim_adpcr(m = 10, n = 765, times = 1000, pos_sums = FALSE, n_panels = 3) # adpcr2 <- sim_adpcr(m = 60, n = 550, times = 1000, pos_sums = FALSE, n_panels = 3) # adpcr2 <- rename_dpcr(adpcr2, exper = "Experiment2") # adpcr3 <- sim_adpcr(m = 10, n = 600, times = 1000, pos_sums = FALSE, n_panels = 3) # adpcr3 <- rename_dpcr(adpcr3, exper = "Experiment3") # # #compare experiments using binomial regression # two_groups_bin <- test_counts(bind_dpcr(adpcr1, adpcr2), model = "binomial") # summary(two_groups_bin) # plot(two_groups_bin) # #plot aggregated results # plot(two_groups_bin, aggregate = TRUE) # #get coefficients # coef(two_groups_bin) # # #this time use Poisson regression # two_groups_pois <- test_counts(bind_dpcr(adpcr1, adpcr2), model = "poisson") # summary(two_groups_pois) # plot(two_groups_pois) # # #see how test behaves when results aren't significantly different # one_group <- test_counts(bind_dpcr(adpcr1, adpcr3)) # summary(one_group) # plot(one_group) ## ---------------------------------------------