Package: popPCR 0.1.1.1
popPCR: Classify Digital PCR Droplets by Fitting Fluorescence Populations
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>.
Authors:
popPCR_0.1.1.1.tar.gz
popPCR_0.1.1.1.zip(r-4.5)popPCR_0.1.1.1.zip(r-4.4)popPCR_0.1.1.1.zip(r-4.3)
popPCR_0.1.1.1.tgz(r-4.4-x86_64)popPCR_0.1.1.1.tgz(r-4.4-arm64)popPCR_0.1.1.1.tgz(r-4.3-x86_64)popPCR_0.1.1.1.tgz(r-4.3-arm64)
popPCR_0.1.1.1.tar.gz(r-4.5-noble)popPCR_0.1.1.1.tar.gz(r-4.4-noble)
popPCR_0.1.1.1.tgz(r-4.4-emscripten)popPCR_0.1.1.1.tgz(r-4.3-emscripten)
popPCR.pdf |popPCR.html✨
popPCR/json (API)
# Install 'popPCR' in R: |
install.packages('popPCR', repos = c('https://zeroh729.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zeroh729/poppcr/issues
- x_multiPop - DPCR sample w/ >=3 populations
- x_onePop - DPCR sample w/ 1 population
- x_twoPop - DPCR sample w/ 2 populations
Last updated 4 years agofrom:e0f0f4c22b. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 13 2024 |
R-4.5-win-x86_64 | OK | Nov 13 2024 |
R-4.5-linux-x86_64 | OK | Nov 13 2024 |
R-4.4-win-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-x86_64 | OK | Nov 13 2024 |
R-4.4-mac-aarch64 | OK | Nov 13 2024 |
R-4.3-win-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-x86_64 | OK | Nov 13 2024 |
R-4.3-mac-aarch64 | OK | Nov 13 2024 |
Exports:calculateConcpopPCRprintSummaryConcprintSummaryFit
Dependencies:mvtnorm
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Target copies estimation | calculateConc |
EM Mixture Model fitting of dPCR droplet fluorescence | popPCR |
Print result summary of popPCR | printSummaryConc |
Print fitted mixture model estimates from popPCR | printSummaryFit |
dPCR sample w/ >=3 populations | x_multiPop |
dPCR sample w/ 1 population | x_onePop |
dPCR sample w/ 2 populations | x_twoPop |