1. Introduction
4.3 Implementation and Results
FCBapp is developed in R with Shiny (Winston Chang, Joe Cheng, JJ Allaire , Yihui Xie 2020). It has been deployed at the RStudio Shinyapps.io cloud server and can be easily accessed through the internet accessed using any operating system or browser (https://alexanderthiemicke.shinyapps.io/deploy9). In addition, the app can be used locally by installing it as an R package. To install the app locally, one can simply run the following command in R: devtools::install_github("alexthie/FCBapp5"). This will try to install all the necessary underlying dependent packages and the complete app and allow the same functionality as the web based interactive browser interface, when installed.
The FCBapp allows users to upload data into the FCBapp, adjust gating parameters and visualize the data within the app. The debarcoded data can be downloaded as a .csv file for further analysis. It uses packages such as the flowcore package (Meur, Hahne, and Ellis 2007). The user needs to provide a ‘key’, which is a simple ‘.txt’ file stating the conditions used in the experiment separated by a space. The flow cytometry data should be an ‘.fcs’ file. Figure 4.1 illustrates the workflow and the layout of the user input of the FCBapp.
The software has been tested using Flow Cytometry data from Jurkat cells that we generated ourselves. In a first step, we uploaded the ‘.fcs’ file to the app and set the parameter for the determination of the main cell population (Figure 4.2). In a next step the barcoded fractions are plotted by the intensity of the stained fluorescent dyes. By adjusting the ‘bw’ parameter, one can control the gating process and the demultiplexing of the barcoded samples (Figure 4.3). In the next step, the populations are automatically assigned to the conditions supplied by the key file and shown in a similarly plotted way (Figure 4.4.) .Finally the distributions of each population are plotted over the conditions in the experiment and the positive population (On-fraction) is plotted as a bar plot on the right (Figure 4.5).
Figure 4.1: Workflow of the FCBapp
A) Upload of data and ‘key’ file, select parameters, visualize and download the processed data. B) User input interface of FCBapp. The app allows to set a threshold for the gate on the main population and for the determination of a positive fraction.
Figure 4.2: Gating using the FCBapp.
After uploading data to the app, the main population can be gated by changing the standard deviation of the bivariate normal distribution. The events collected by the flow cytometer and stored in the ‘.fcs’ file are plotted for the forward vs. sideward scatter. The red circle indicates the gated cells and the number in the plot indicates the percentage gated relative to all events.
Figure 4.3: Debarcoding of fluorescently labeled cells using the FCBapp.
Jurkat cells are plotted for the intensity of the fluorescent dye labels that were used to achieve the barcoding. The red circles indicate the gates for the individual barcoded cell populations.
Figure 4.4: The debarcoded populations are assigned to the conditions used in the experiment.
Jurkat cells measured by Flow cytometry are plotted for the intensity of Pacific-Blue and Pacific-Orange. The colors indicate the timepoint samples each population corresponds to.
Figure 4.5: Visualizing the distributions of the debarcoded fractions.
The left panel shows the single cell distributions of the debarcoded flow cytometry data.
Jurkat cells have been barcoded and stained with an antibody for cleaved PARP, exposed to add. 300 mosmol/l NaCl and fixed at timepoints indicated on the y-axis (min). The red line represents the threshold for the ‘OnFraction’ that can be controlled by the app. The right panel represents the ‘OnFraction’ as percentage of positively stained cells for each population.
Figure 4.6: Sample data from the Flowrepository debarcoded by the FCBapp.
Screenshot of the FCBapp interface showing flow cytometry data of PBMCs deposited by the authors of Davies et al. (Davies et al. 2016) that has been automatically debarcoded using the FCBapp.
Figure 4.7: Assigning conditions to the Sample data from the Flowrepository.
Screenshot of the FCBapp interface showing the assignment of flow cytometry data of PBMCs deposited by the authors of Davies et al. (Davies et al. 2016) to the conditions used in the respective experiments as given in the uploaded key file.
We aimed to test the software with publicly available data. To demonstrate the feasibility of the FCBapp, we tested the software on a publicly available dataset (Davies et al. 2016) that was deposited to the flowrepository (Spidlen et al. 2012). We downloaded the data, uploaded it to the FCBapp and generated a key file giving the conditions used in the experiment. Figure 4.6 shows the debarcoding result of the PBMC data from the publicly available dataset (Davies et al. 2016). The debarcoded populations are then assigned to the treatment conditions used in the experiment (Figure 4.7). The debarcoded data can then be visualized as distributions in the app and downloaded as a ‘.csv’ file for further analysis. This approach shows that the FCBapp is able to debarcode new data from different cells (PBMCs vs. Jurkat cells) and a different number of barcoded populations (9 vs. 12).