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Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree

If you use this work in published research, please cite:

Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree

This repo contains source code and data to produce Supplementary Material of the above paper.

https://github.com/YuLab-SMU/Supplemental_ggtree_placement

1. exampledata

This directory contains example input data files for phylogenetic analysis and tree placements.

Holomycota:

Contains jplace and CSV files related to the Holomycota dataset.

  • HolomycotaV4_alignedtrim.jplace: Placement file for the Holomycota phylogenetic analysis.
  • V4_group.csv: Associated metadata for the groups in the Holomycota dataset.

Mitsi:

Contains files from the Mitsi dataset.

  • rsbl20190182supp2.jplace: Placement file for the Mitsi dataset.
  • rsbl20190182supp7.tre: Tree file corresponding to the placement data.

subtree:

Contains data for a specific subtree analysis.

  • Amt_tiplabel.csv: Tip label information for the Amt subtree.
  • pplacer_Amt_subtree.jplace: Placement file for the Amt subtree analysis.

2. pdf

This directory contains PDF files of the figures generated from the analysis.

  • Fig1.pdfWorkflow diagram of treeio and ggtree in processing phylogenetic placement data, Fig2.pdf, Fig3.pdf,Fig4.pdf: Figures representing various visualizations of the phylogenetic analysis. FigS1.pdf:Supplementary figure showing detailed performance evaluations of treeio, including runtime and memory efficiency when processing large phylogenetic trees with diverse placement scenarios.

3. Rmarkdown

This directory contains an R Markdown file used for generating the supplementary files for the project.

  • header.tex: LaTeX header for formatting the supplementary file.
  • supplementary_filev2.Rmd: R Markdown source file for generating the supplementary file.
  • supplementary_filev2.pdf: PDF version of the supplementary file.

4. tiff

This directory contains TIFF versions of the figures for high-quality image export.

  • Fig2.tiff, Fig3.tiff, Fig4.tiff: High-resolution images of the figures in TIFF format.

5. simulated_data

This directory contains files and scripts for simulated data analysis.

  • scripts
    Contains R scripts for generating and visualizing simulated data:
    • plot_simulated_jplace.R.r: Script for visualizing simulated .jplace files.
    • run_simulate_jplace.R: Script for generating simulated .jplace data.
  • simulated_jplace
    Contains simulated .jplace files with different configurations:
    • simulate_tips100000_placement_nrow_1000000.jplace
    • simulate_tips100000_placement_nrow_100000.jplace
    • simulate_tips100000_placement_nrow_10000.jplace
    • simulate_tips100000_placement_nrow_1000.jplace
    • (and others with similar naming patterns).
  • src
    Contains alternative versions of scripts for generating and visualizing simulated data:
    • plot_simulated_jplace.R.r
    • run_simulate_jplace.R
  • Other Files
    Test .jplace files with different sample sizes and row configurations for validation:
    • test_jp_1k_1kp.jplace, test_jp_1k_10kp.jplace, test_jp_1k_100kp.jplace, test_jp_1k_1000kp.jplace
    • test_jp_10k_1kp.jplace, test_jp_10k_10kp.jplace, test_jp_10k_100kp.jplace, test_jp_10k_1000kp.jplace
    • test_jp_50k_1kp.jplace, test_jp_50k_10kp.jplace, test_jp_50k_50kp.jplace, test_jp_50k_100kp.jplace
    • test_jp_100k_1kp.jplace, test_jp_100k_10kp.jplace, test_jp_100k_50kp.jplace, test_jp_100k_100kp.jplace
    • (and others with similar naming patterns).

Usage

  • The exampledata directory contains the input data files used in the analysis.
  • Figures are stored in both PDF and TIFF formats for use in publications or presentations.
  • The Rmarkdown directory contains the source file for generating the supplementary materials, which can be edited or recompiled if needed.

Requirements

To reproduce the figures and analyses, you will need:

  • R with necessary packages such as ggtree, treeio, dplyr, and ggplot2.
  • LaTeX for compiling the R Markdown file to PDF.