CLAWS dPCR plotting script

This repository contains a simple R workflow used to visualize raw digital PCR (dPCR) partition data for the CLAWS assay.

The script was developed to support analyses presented in:

Box et al.
5′ Leader Defects 1 Drive Persistent HIV-1 Viremia on Long-Term ART, Nature Communications, 2026

The workflow: - imports raw dPCR partition data, from the Qiacuity instrument but it can be adapted for any dPCR system with RFU values of each partition - subsamples double-negative partitions for visualization clarity and to generate lightr images for publication - generates publication-style 2D fluorescence plots using the tidyplots package.

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Repository contents

CLAWS_dPCR_plotting.R      Main plotting script
claws_example_data.csv     Example input dataset
subsampled_example.csv          Example output dataset
LICENSE                    MIT license
README.md                  Documentation

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Requirements

R version

Tested with:

R >= 4.3

Required packages

install.packages(c(
  "tidyplots",
  "dplyr",
  "ggplot2"
))

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Input data format

The input .csv file should contain, at a minimum, the following columns:

Column	Description
atg	Fluorescence intensity for ATG probe
msd	Fluorescence intensity for MSD probe
double_neg	Indicator for double-negative partitions

Example:

atg	msd	double_neg
12.3	8.1	0
55.7	34.2	1

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Usage

Place the input .csv file in the working directory and run:

source("CLAWS_dPCR_plotting.R")

The script will: 1. import the data, 2. subsample double-negative partitions, 3. save the subsampled dataset, 4. generate a dPCR scatter plot.

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Output

The workflow generates: - a subsampled .csv dataset, - a publication-style dPCR partition plot.

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Notes

This software is purely for visualization purposes, use the original instrument's software to set thresholds and determine positive partitions

Double-negative partitions are randomly subsampled to improve plot readability while preserving all positive partitions.

Random subsampling is reproducible through a fixed random seed.

This function can be skipped if one wants to preserve all partitions.

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Dependencies

This workflow uses the tidyplots R package:

Engler JB. tidyplots empowers life scientists with easy code-based data visualization. iMeta. 2025. https://doi.org/10.1002/imt2.70018.

tidyplots repository: https://github.com/jbengler/tidyplots

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License

This repository is distributed under the MIT License.

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Author

Francesco R. Simonetti
Johns Hopkins University
Division of Infectious Diseases