| Title: | Classify Digital PCR Droplets by Fitting Fluorescence Populations |
| Version: | 0.1.1.1 |
| Description: | Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the 'EMMIXskew' R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>. |
| Imports: | mvtnorm |
| Depends: | graphics, stats, methods, grDevices, R (≥ 2.10) |
| License: | GPL (≥ 3) |
| LazyLoad: | yes |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | yes |
| RoxygenNote: | 7.1.1 |
| Packaged: | 2021-03-09 14:44:42 UTC; Jeh |
| Author: | Joyce Emlyn Guiao [aut, cre] |
| Maintainer: | Joyce Emlyn Guiao <joyce_emlyn_guiao@dlsu.edu.ph> |
| Repository: | CRAN |
| Date/Publication: | 2021-03-09 23:50:02 UTC |
Target copies estimation
Description
Mean target copies per partition (lambda) is derived using Poisson distribution as lambda = -ln(nneg / ntot). Target copies in sample is then calculated as conc = lambda * volSamp/(volDrp * 1000).
Usage
calculateConc(nneg, ntotal, volSamp, volDrp)
Arguments
nneg |
numeric, negative droplet count |
ntotal |
numeric, total droplet count |
volSamp |
numeric, sample volume in microliter |
volDrp |
numeric, droplet (or partition) volume in nanoliter |
Value
Returns a list with 2 named items lambda and conc
lambda - numeric, vector of mean target copies per partition (lambda) and its lower and upper 95% confidence interval
conc - numeric, vector of target copies in sample (based on the given sample volume (
volSamp) and droplet volume (volDrp)) and its lower and upper 95% confidence interval
Examples
estimates <- calculateConc(5000, 20000, volSamp = 20, volDrp = 0.85)
estimates
# Output:
# $lambda
# lambda lower upper
# 1.386294 1.362289 1.410299
#
# $conc
# conc lower upper
# 32618.69 32053.87 33183.51
EM Mixture Model fitting of dPCR droplet fluorescence
Description
Estimates target concentration by counting positive droplets with Poisson correction. Positive, negative, and rain populations are fitted using EM. Droplets are then classified using Maximum A Posteriori rule
Usage
popPCR(
x,
dist,
volSamp = 20,
volDrp = 0.85,
maxComponents = Inf,
negProbThres = 1e-07,
useOnlyNegProbThres = FALSE
)
Arguments
x |
numeric, vector of droplet fluorescence amplitude |
dist |
character, distribution of the mixture models ("normal", "skewed-normal", "t", "skewed-t") |
volSamp |
numeric, sample volume in microliter |
volDrp |
numeric, droplet (or partition) volume in nanoliter |
maxComponents |
numeric, maximum number of components (e.g. populations) |
negProbThres |
numeric, if only one population was detected, then its assumed as a negative population. Droplets will be classified as positive if its probability given the population < negProbThres. |
useOnlyNegProbThres |
logical, if TRUE, then droplets will be classified as positive if its probability given the leftmost population < negProbThres. Default is FALSE, i.e. classification is done by Maximum A Posteriori rule. |
Value
Returns a result.popPCR S4 class object with attributes
classification - character, vector of droplet classification
dist - character, user-specified parameter for the mixture model
dropletCount - list, droplet classification count
em - list, returned value of EMMIXskew's EmSkew()
estConc - list, estimated target concentration as lambda and sample concentration (with 95% CI)
G - numeric, number of components fitted
memberProb - list, component membership probability of all droplets
Examples
library(popPCR)
# Plot histograms of available data
hist(x_onePop, breaks = 100)
hist(x_twoPop, breaks = 100)
hist(x_multiPop, breaks = 100)
# ---- Mixture model fitting ---- #
# Example 1. One population sample
result <- popPCR(x_onePop, dist = "t")
printSummaryConc(result)
# Output:
# 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 ] )
printSummaryFit(result)
# Output:
# Results of fitting a 1-component t mixture model
#
# Negative Population
# Mix prop. : 1
# Mu : 1024.1614
# Sigma : 35253.1747
# Dof : 2.005
# (Option) increase negProbThres to classify negative droplets more strictly
result <- popPCR(x_onePop, dist = "t", negProbThres = 1e-4)
printSummaryConc(result)
# Output:
# 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 ] )
# Example 2. Two population sample
result <- popPCR(x_twoPop, dist = "t")
printSummaryConc(result)
# Output:
# 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 ] )
printSummaryFit(result)
# Output:
# 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
# Example 3. Multiple population sample
result <- popPCR(x_multiPop, dist = "t", maxComponents = 4)
printSummaryConc(result)
# Output:
# 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(stats::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 result summary of popPCR
Description
Summarizes the number of populations detected, total droplets, and number of classified positive, negative, and rain droplets. Also calculates the target copies in sample and the mean target copies per partition (lambda).
Usage
printSummaryConc(result.popPCR)
Arguments
result.popPCR |
returned value of popPCR() |
Examples
result <- popPCR(x_twoPop, dist = "t")
printSummaryConc(result)
# Output:
# 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 ] )
Print fitted mixture model estimates from popPCR
Description
Summarizes the number of populations fitted and their estimate distribution parameters. If only 1 population was detected, then it is assumed and is identified to be a negative population. If 2 populations were detected, then the leftmost is identified as the Negative Population and the rightmost is the Positive Population. If 3 or more populations were detected, then the populations between the leftmost and the rightmost will be considered as Rain Populations; which are numbered to make it identifiable in case of multiple Rain Populations (i.e. Rain (1) and Rain (2)).
Usage
printSummaryFit(result.popPCR)
Arguments
result.popPCR |
returned value of popPCR() |
Examples
result <- popPCR(x_twoPop, dist = "t")
printSummaryFit(result)
# Output:
# 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
result <- popPCR(x_multiPop, dist = "t", maxComponents = 4)
printSummaryFit(result)
# Output:
# Results of fitting a 4-component t mixture model
#
# Negative Population
# Mix prop. : 0.6896
# Mu : 1452.1416
# Sigma : 12526.8931
# Dof : 21.3612
#
# Rain (1) Population
# Mix prop. : 0.142
# Mu : 2142.1118
# Sigma : 10762.5474
# Dof : 186.2947
#
# Rain (2) Population
# Mix prop. : 0.1457
# Mu : 5119.0039
# Sigma : 334959.2499
# Dof : 2.3626
#
# Positive Population
# Mix prop. : 0.0227
# Mu : 8505.9682
# Sigma : 192858.9044
# Dof : 149.8677
dPCR sample w/ >=3 populations
Description
The reaction with ID 373 in the Dataset.zip provided in the repository from Lievens et. al. (2017)
Usage
x_multiPop
Format
A numeric vector with 1814 droplet fluorescence
Source
https://github.com/Gromgorgel/ddPCR/blob/master/Dataset.zip
dPCR sample w/ 1 population
Description
Simulated dataset with very high presence of rain and true mean copies per partition of 0.1.
Usage
x_onePop
Format
A numeric vector with 8000 droplet fluorescence
dPCR sample w/ 2 populations
Description
The reaction with ID 9 in the Dataset.zip provided in the repository from Lievens et. al. (2017)
Usage
x_twoPop
Format
A numeric vector with 10254 droplet fluorescence