dpcR (version 0.3)

test_counts: Test counts

Description

The test for comparing counts from two or more digital PCR experiments.

Usage

test_counts(input, model = "ratio", conf.level = 0.95)

Arguments

input

object of class adpcr or ddpcr with "nm" type.

model

may have one of following values: binomial, poisson, prop, ratio. See Details.

conf.level

confidence level of the intervals and groups.

Value

an object of class count_test.

Details

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.

References

Bretz F, Hothorn T, Westfall P, Multiple comparisons using R. Boca Raton, Florida, USA: Chapman & Hall/CRC Press (2010).

See Also

Functions used by test_counts:

• glm,
• glht,
• cld,
• prop.test,
• rateratio.test

GUI presenting capabilities of the test: test_counts_gui.

Examples

#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)
# ## End(Not run)