Drone imagery and metabarcoding in field diagnostics

This repository contains the supplementary code and intermediary data for the metabarcoding data analysis performed as part of the study 'Drone imagery and metabarcoding as diagnostic tools of poor growth in potato fields' by Persdatter et al.

Content

The repository includes the following contents:

1. Code:

   • metabarcoding_analysis.R: R script for the metabarcoding data analysis, including data preprocessing, diversity analysis, and statistical testing.
   • metabarcoding_functions.R: Custom functions used in the metabarcoding data analysis.

2. Data:

   • raw_metabarcoding_data.csv: Raw metabarcoding data, including OTU (Operational Taxonomic Unit) counts for each sample.
   • metadata.csv: Metadata for the samples, including information such as field location, plant growth status, and other relevant variables.

3. README.md: This file, providing an overview of the repository and instructions for use.

Usage

1. Clone the repository to your local machine:

   git clone https://gitlab.nibio.no/simeon/persdatter-et-al-2025-metabarcoding.git

2. Ensure you have the necessary R packages installed. All necessary dependencies required by the analysis scripts are listed in the top chunks of the respective scripts.

3. Obtain the raw read data from ENA

4. Open the R Markdown file and run the code to reproduce the metabarcoding data analysis.

Citing this Repository

If you use the code or data from this repository in your own work, please cite the original publication:

To be updated upon publication

Additionally, please acknowledge the use of this supplementary repository by citing it as follows:

MP Tangvik et al. (2024). Supplementary code and data for 'Drone imagery and metabarcoding as diagnostic tools of poor growth in potato fields' [Data set]. GitLab. https://gitlab.nibio.no/simeon/persdatter-et-al-2025-metabarcoding

Contact

For any questions or issues related to this repository, please contact Simeon Lim Rossmann or Marte Persdatter Tangvik