test_counts {dpcR} | R Documentation |
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 |
|
model |
may have one of following values: |
conf.level |
confidence level of the intervals and groups. |
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.
Value
an object of class count_test
.
Note
Mean number of template molecules per partition and its confidence intervals will vary depending on input.
Author(s)
Michal Burdukiewicz, Stefan Roediger, Piotr Sobczyk.
References
Bretz F, Hothorn T, Westfall P, Multiple comparisons using R. Boca Raton, Florida, USA: Chapman & Hall/CRC Press (2010).
Examples
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)