Classify Digital PCR Droplets by Fitting Fluorescence Populations.

popPCR

R package for fitting droplet fluorescence populations of dPCR amplitude data using Expectation Maximization.

Installation

Install from CRAN

install.packages("popPCR")

Install from GitHub

install.packages("devtools")
devtools::install_github("Zeroh729/popPCR")

Usage

popPCR(x, dist = "t")

3 example datasets are available upon import

library(popPCR)

hist(x_onePop, breaks = 100)     # dPCR sample w/ 1 population
hist(x_twoPop, breaks = 100)     # dPCR sample w/ 2 populations
hist(x_multiPop, breaks = 100)   # dPCR sample w/ >=3 populations

Case 1. One population sample

result <- popPCR(x_onePop, dist = "t")
#        Populations detected : 1
#        Total droplets : 8000
#        Positive : 1 (0.01%)
#        Negative : 7999 (99.99%)
#
#        Target copies in sample          : 2.9414 ( 95% CI: [ -2.8237 , 8.7064 ] )
#        Mean target copies per partition : 1e-04 ( 95% CI: [ -1e-04 , 4e-04 ] )

# Increasing negProbThres makes negative classification stricter
result <- popPCR(x_onePop, dist = "t", negProbThres = 1e-4)  
#        Populations detected : 1
#        Total droplets : 8000
#        Positive : 691 (8.64%)
#        Negative : 7309 (91.36%)
#
#        Target copies in sample          : 2125.5312 ( 95% CI: [ 1966.9936 , 2284.0688 ] )
#        Mean target copies per partition : 0.0903 ( 95% CI: [ 0.0836 , 0.0971 ] )

Case 2. Two population sample

result <- popPCR(x_twoPop, dist = "t")
#        Populations detected : 2
#        Total droplets : 10254
#        Positive : 8693 (84.78%)
#        Negative : 1561 (15.22%)
#
#        Target copies in sample          : 44290.3819 ( 95% CI: [ 43215.6408 , 45365.1231 ] )
#        Mean target copies per partition : 1.8823 ( 95% CI: [ 1.8367 , 1.928 ] )

Case 3. Multiple population sample

result <- popPCR(x_multiPop, dist = "t", maxComponents = 4)
#        Populations detected : 4
#        Total droplets : 1814
#        Positive : 44 (2.43%)
#        Negative : 1252 (69.02%)
#        Rain (1) : 258 (14.22%)
#        Rain (2) : 260 (14.33%)
#
#        Target copies in sample          : 8724.5195 ( 95% CI: [ 7999.0578 , 9449.9812 ] )
#        Mean target copies per partition : 0.3708 ( 95% CI: [ 0.34 , 0.4016 ] )

# In the output above, we see 2 rain populations! Let's examine its plot.
plot(density(x_multiPop))

# We can see that Rain (1) is very close to the Negative population.
# Let's include droplets in Rain (1) in the negative droplet count.
nNegative <- result@dropletCount$neg + result@dropletCount$rain1
nTotal <- result@dropletCount$total

# Re-estimate concentration as follows
newEstimates <- calculateConc(nNegative, nTotal, volSamp = 20, volDrp = 0.85)
newEstimates
#    Output:
#       $lambda
#          lambda     lower     upper
#       0.1834247 0.1627763 0.2040731
#
#       $conc
#           conc    lower    upper 
#       4315.875 3830.031 4801.719 

Print results summary

result <- popPCR(x_twoPop, dist = "t")
printSummaryFit(result)
#        Results of fitting a 2-component t mixture model
#
#        Negative Population
#        Mix prop. : 0.1522
#        Mu        : 2136.7435
#        Sigma     : 4126.8357
#        Dof       : 12.3562
#
#        Positive Population
#        Mix prop. : 0.8478
#        Mu        : 7580.1275
#        Sigma     : 42621.1894
#        Dof       : 2.415

Available dist values : normal, skewed-normal, t, and skewed-t
Use ?popPCR to view complete documentation.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

GNU GPLv3.0