ddPCRvis is an interactive, web based GUI for the ddPCRclust algorithm. It was developed using the web application framework Shiny.
An online version of the app is available under https://bibiserv.cebitec.uni-bielefeld.de/ddPCRvis/
If you want to use it locally, you can simply clone this repository and run the app using RStudio. Please note, that the ddPCRclust package needs to be installed on your machine.
You also need to install the following packages:
install.packages(c('shiny', 'shinyjs', 'rhandsontable', 'jsonlite', 'plotly'), repos='https://cran.rstudio.com/')To run the app on your local computer, use RStudio or run the following command and replace ~ with the location of your ddPCRvis folder.
R -e "shiny::runApp('~/ddPCRvis')"Since you found your way here, I think it's safe to assume that you are familiar with websites and how to navigate them. ddPCRvis works exactly the same. On the top, you have the navigation bar:
Notice the help button on the right side. Click it and it will explain everything you need to know on the current page. On the left side, a sidebar contains all available input fields for the current view. Here is an example for the 'Upload files' view:
To get started, you have to upload your data. Since one experiment most likely consists of many different files, naming them apropriately is important in order to keep things organized. We chose to use a unique identifier in each filename of the form "^[[:upper:]][[:digit:]][[:digit:]]$" (A01, A02, A03, B01, B02, ...), which is usually included automatically by the ddPCR machine.
In order to get the best results, it is recommended to set up a template file for your experiment. The template has to be a csv file with a header, which contains information about each of the raw data files according to their unique identifier, as explained above.
> Name of your experiment, channel1=HEX, channel2=FAM, annotations(date, experimentor, etc)
| Well | Sample type | No of markers | Marker 1 | Marker 2 | Marker 3 | Marker 4 |
|---|---|---|---|---|---|---|
| B01 | Blood | 4 | a | b | c | d |
| G01 | FFPE | 4 | a | b | c | d |
| F02 | Blood | 3 | a | c | d | |
| D03 | FFPE | 3 | a | c | d | |
| A04 | FFPE | 4 | a | b | c | d |
| G07 | Cell line | 3 | a | c | d | |
| G08 | Cell line | 3 | a | c | d | |
| E09 | FFPE | 2 | c | d |
The raw data should be csv files. Each file represents a two-dimensional data frame. Each row within the data frame represents a single droplet, each column the respective intensities per colour channel (please make sure your decimal symbol is a point):
| Ch1 Amplitude | Ch2 Amplitude |
|---|---|
| 2360.098 | 6119.26953 |
| 2396.3916 | 1415.31665 |
| 2445.838 | 6740.79639 |
| 2451.63867 | 1381.74683 |
| 2492.55884 | 1478.19617 |
| 2519.6355 | 7082.25049 |
| ⋮ | ⋮ |
After you hit 'Start Analysis', the ddPCRclust algorithm will run in the background. Once the computation is completed (allow ~5 seconds per file), you will be redirected to the next page, where you can inspect the result and spot any errors or other irregularities. Depending on your internet connection and the number of files you submitted, loading the plots in the browser can take a little while, so please be patient.
You can manually edit the clustering result for each well individually. Once you are satisfied, click on 'Count droplets' to continue. You can then inspect and download the droplet counts for each cluster, as well as the individual copies per droplet (CPDs) per marker, if you provided this information in the template.
Lastly, ddPCRvis provides you an interactive visualization to explore the results and compare markers vs stable controls.
Brink, Benedikt G., et al. "ddPCRclust: An R package and Shiny app for automated analysis of multiplexed ddPCR data." Bioinformatics (2018).
https://www.ncbi.nlm.nih.gov/pubmed/29534153
ddPCRvis is licensed under the Artistic License 2.0.