Dataset: Dimensionality of infection networks among viruses infecting microbial eukaryotes and bacteria from Edwards and Hayward (2024)

Name: Quantitative analysis of viral infection networks using previously published phytoplankton-virus networks newly compiled for results publication Edwards and Hayward (2024)
Published: 2024-03-26
Keywords: oceans

View Data: Data not available yet

Cite This Dataset

Temporal Extent: 1994 - 2023

Project:

Giant viruses in the open ocean: Is large size adaptive where cells are scarce? (GVs NPSG)

Principal Investigator:

Kyle F. Edwards (University of Hawaii at Manoa)

BCO-DMO Data Manager:

Amber D. York (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2024-03-26

Restricted:

Validated:

Current State:

Data not available

Quantitative analysis of viral infection networks using previously published phytoplankton-virus networks newly compiled for results publication Edwards and Hayward (2024)

Abstract:

This dataset contains results of a quantitative analysis performed using previously published phytoplankton-virus networks newly compiled for results publication Edwards and Hayward (2024). Network dimensionality (in terms of the minimum embedding dimensionality) was analyzed using R code with input files containing 11 phytoplankton-virus cross-infection matrices collected from published sources.

Expand/Collapse All

Methods & Sampling

Cross-infection matrices for viruses infecting eukaryotic phytoplankton were collected from published sources.

The table provided in the "Data Files" section for this dataset corresponds to Table S1 of Edwards and Hayward (2024) and includes the results of a quantitative analysis of each viral infection network. Citations for the eukaryote-virus networks are listed in the column "Reference" with full citations listed in the "Related Datasets" section. The bacteria-virus networks were taken from a previous compilation: Flores et al. (2011).

Data Processing Description

The supplemental .zip packages containing R-code that performed the analysis and input files containing the compiled sources are available from the publication (Edwards, K. (2024), https://doi.org/10.6084/m9.figshare.24991041).

"R Code for analyzing network dimensionality" supplementary_file_S1.zip contains three scripts that analyze the dimensionality of infection networks.

(1) The script ‘for_publication_nullmodel.R’ takes an infection matrix and uses a null model to test whether the infection matrix can be sorted in one dimension better than expected by chance. As noted in the script, the script assumes that the matrix is a csv file formatted such that rows correspond to viruses, columns correspond to hosts, the first column contains virus strain labels, and the first row contains host strain labels. 1 = infection, 0 = no infection. The script was written in R 4.3.2 and also uses a package called future.apply for parallelization.

(2) The script ‘for_publication_find_minimum_dimensionality.R’ takes and infection matrix and finds the smallest number of dimensions in which an infection matrix can be sorted with zero errors. The data input is the same as the null model script.

(3) The script ‘for_publication_dimensionality_plot_functions.R’ contains plotting functions that are referenced by the second script.

Other details of how the code works are included as annotations within the scripts themselves.

"Phytoplankton-virus infection networks" supplementary_file_S2.zip contains 11 phytoplankton-virus networks stored as .csv files, where the rows are virus strains and the columns are host strains. Within the matrix a value of 1 indicates that the virus strain can infect the phytoplankton strain, while 0 indicates that it cannot. The csv files are named with the last name of the first author of the publication, followed by the date of publication.

BCO-DMO Processing

* Sheet 1 of submitted file "Supplementary_Table_S1.xlsx" was imported into the BCO-DMO data system for this dataset. Parameter descriptions below the data table were removed from the data file and added in the "Parameters" section.
* previously published and DOI'ed supplemental files were not republished as part of this datasets and instead were linked as related publications and described in the metadata.
* Taxonomic names used in the data table were matched with identifiers using the World register of marine species (WoRMS) with gaps filled in by searching for non-marine taxa at ITIS.gov.