# twoddpcr [twoddpcr](https://bioconductor.org/packages/twoddpcr/) is an R/Bioconductor package for Droplet Digital PCR (ddPCR) analysis. The package is named for its ability to classify two channel ddPCR data (the amalgamation of "2-d" and "ddPCR"). Example usage of the package with a sample dataset is included in the package's [vignette](https://bioconductor.org/packages/release/bioc/vignettes/twoddpcr/inst/doc/twoddpcr.html), where the Appendix also explains how to extract raw droplet data from Bio-Rad's QuantaSoft. ## Installing the `twoddpcr` package The package can be installed from Bioconductor using: ``` install.packages("BiocManager") BiocManager::install("twoddpcr") ``` Alternatively, it can be installed from GitHub using: ``` library(devtools) install_github("CRUKMI-ComputationalBiology/twoddpcr") ``` Another alternative is to install the package from source: ``` install.packages("</path/to/twoddpcr/>", repos=NULL, type="source") ``` ## Loading the `twoddpcr` package Once the package has been installed, it can be loaded in the usual way: ``` library(twoddpcr) ``` ## `ddpcrPlate` objects The package revolves around the use of `ddpcrPlate` objects, which stores droplet amplitude data for the wells in the plate. The `ddpcrPlate` object also keeps track of the various classifications of the droplets. To use your own droplet data and store it in a variable named `plate`, use: ``` plate <- ddpcrPlate("amplitudes/") ``` Here, we have assumed that your droplet amplitude CSVs are stored in the `amplitudes` directory inside the working directory. ## Classifying using the k-means clustering If your data has four distinct clusters, the k-means clustering algorithm can be used: ``` plate <- kmeansClassify(plate) ``` This method can also take the `centres` parameter as a matrix to adjust the approximate centres of each cluster. This parameter can also be the number of clusters instead of a matrix. See `?kmeansClassify` for more details. ## Plotting classifications Once the droplets have been classified, they can be plotted using the `dropletPlot` method. To use this, the names of the available classification methods can be obtained by: ``` getCommonClassMethod(plate) ``` There should be a `kmeans` entry for k-means clustering. To plot this, use: ``` dropletPlot(plate, cMethod="kmeans") ``` ## Adding "rain" Droplets between the main clusters of droplets will possibly have ambiguous classifications. These droplets are colloquially known as "rain". To do this, use: ``` plate <- mahalanobisRain(plate, maxDistances=list(NN=35, NP=30, PN=30, PP=30)) ``` Some trial-and-error is required for setting the `maxDistances` parameter. ## Summaries Since ddPCR samples are partitioned into droplets, the molecules are assumed to be Poisson distributed. With this, the actual number of starting fragments can be estimated: ``` plateSummary(plate, cMethod="kmeansMahRain") ``` ## Shiny-based GUI for non-R users A Shiny app is included in the package, which provides a GUI that allows interactive use of the package for ddPCR analysis. This can be run from an interactive R session using: ``` shinyVisApp() ``` It can also be accessed at [http://shiny.cruk.manchester.ac.uk/twoddpcr/](http://shiny.cruk.manchester.ac.uk/twoddpcr/). If you wish to run the app from your own server, instructions are given in the [vignette](https://bioconductor.org/packages/devel/bioc/vignettes/twoddpcr/inst/doc/twoddpcr.html#shiny-based-gui-for-non-r-users). # Citing `twoddpcr` If you use the `twoddpcr` package in your work, please cite the [Bioinformatics paper](http://dx.doi.org/10.1093/bioinformatics/btx308). The citation can be obtained within R using: ``` citation("twoddpcr") ```