README.md

In CRUKMI-ComputationalBiology/twoddpcr: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules

twoddpcr

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, 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/.

If you wish to run the app from your own server, instructions are given in the vignette.

Citing twoddpcr

If you use the twoddpcr package in your work, please cite the Bioinformatics paper. The citation can be obtained within R using:

citation("twoddpcr")

CRUKMI-ComputationalBiology/twoddpcr documentation built on Feb. 14, 2021, 9:18 p.m.