Methods for Function summary
summary allowing printing summaries
objects of the class
adpcr to or
object of class
The function prints a summary of the dPCR reaction, including k (number of positive chambers), n (total number of chambers), estimated lambda and m (number of molecules per plate), as well as confidence intervals for the last two variables.
The data frame with estimated values of lambda, m and corresponding confidence intervals.
If summary is used on an object containing results of many
experiments, all experiments would be independently summarized. Currently
supported only for objects of class
Michal Burdukiewicz, Stefan Roediger.
Bhat S, Herrmann J, Corbisier P, Emslie K, Single molecule detection in nanofluidic digital array enables accurate measurement of DNA copy number. Analytical and Bioanalytical Chemistry 2 (394), 2009.
Dube S, Qin J, Ramakrishnan R, Mathematical Analysis of Copy Number Variation in a DNA Sample Using Digital PCR on a Nanofluidic Device. PLoS ONE 3(8), 2008.
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# array dpcr # Simulates a chamber based digital PCR with m total number of template molecules # and n number of chambers per plate and assigns it as object ptest of the class # adpcr for a single panel. The summary function on ptest gets assigned to summ # and the result with statistics according to Dube et al. 2008 and Bhat et al. 2009 # gets printed. ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 1) summ <- summary(ptest) #save summary print(summ) # multiple experiments # Similar to the previous example but with five panels ptest <- sim_adpcr(m = 400, n = 765, times = 5, dube = FALSE, n_panels = 5) summary(ptest) # droplet dpcr - fluorescence # Simulates a droplet digital PCR with m = 7 total number of template molecules # and n = 20 number of droplets. The summary function on dropletf gives the # statistics according to Dube et al. 2008 and Bhat et al. 2009. The fluo parameter # is used to change the smoothness of the fluorescence curve and the space between # two consecutive measured peaks (droplets). dropletf <- sim_ddpcr(m = 7, n = 20, times = 5, fluo = list(0.1, 0)) summary(dropletf) # droplet dpcr - number of molecules # Similar to the previous example but with five panels but without and modifications # to the peaks. droplet <- sim_ddpcr(m = 7, n = 20, times = 5) summary(droplet) # Visualize the results of dropletf and dropletf # The curves of dropletf are smoother. par(mfrow = c(1,2)) plot(dropletf, main = "With fluo parameter", type = "l") plot(droplet, main = "Without fluo parameter", type = "l")
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