Example Plot Source Citations

Reviewed on 2026-05-28.

This note maps the retained example/reference plots in python_refactor_goal_sources/goal_plots/ to the primary papers, data sources, or documentation pages that produced or motivated them. The clean gallery images in python_refactor_goal_sources/generated_plots/clean/ use deterministic local TSV/JSON fixtures to recreate the visual structure of those sources; the Python/fuc AML fixtures are compact derivatives of upstream sbslee/fuc-data.

Local source files reviewed:

  • python_refactor_goal_sources/source_info_for_training.md

  • python_refactor_goal_sources/config.yaml

  • python_refactor_goal_sources/fuc_sources/manifest.json

  • python_refactor_goal_sources/ggoncoplot/paper/paper.md

  • python_refactor_goal_sources/ggoncoplot/paper/paper.bib

  • python_refactor_goal_sources/ggoncoplot/inst/CITATION

Plot-To-Source Map

Goal plots

Example family

Read/cite first

Source URL

goal_plot_01.png

ggoncoplot paper Figure 1, TCGA BRCA

El-Kamand et al. 2025; Goldman et al. 2020; Ellrott et al. 2018; TCGA Research Network

https://doi.org/10.21105/joss.07390

goal_plot_02.png - goal_plot_05.png

ggoncoplot README/pkgdown oncoplot examples

El-Kamand et al. 2025; ggoncoplot documentation

https://selkamand.github.io/ggoncoplot/

goal_plot_06.png

ggoncoplot GBM paper asset

El-Kamand et al. 2025; TCGA/MC3-derived GBM test data

https://github.com/selkamand/ggoncoplot/tree/main/paper

goal_plot_07.png

Chinese breast tumor oncoplot

Zhang et al. 2019

https://pmc.ncbi.nlm.nih.gov/articles/PMC6526269/

goal_plot_08.png

Metastatic cutaneous squamous cell carcinoma alteration matrix

Thind et al. 2022

https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.919118/full

goal_plot_09.png

Glioblastoma clinical/molecular panel

Hoogstrate et al. 2023

https://www.sciencedirect.com/science/article/pii/S1535610823000478

goal_plot_10.png - goal_plot_13.png

AML/fuc oncoplot tutorial examples

Lee, fuc tutorials and fuc-data TCGA-LAML fixtures

https://sbslee-fuc.readthedocs.io/en/latest/tutorials.html

Directly Used Papers

These are the main papers behind source figures used as reference/example plots.

  1. El-Kamand S, Quinn JMW, Cowley MJ. ggoncoplot: an R package for interactive visualisation of somatic mutation data from cancer patient cohorts. Journal of Open Source Software. 2025;10(115):7390. doi:10.21105/joss.07390. https://doi.org/10.21105/joss.07390

  2. Zhang G, Wang Y, Chen B, et al. Characterization of frequently mutated cancer genes in Chinese breast tumors: a comparison of Chinese and TCGA cohorts. Annals of Translational Medicine. 2019;7(8):179. doi:10.21037/atm.2019.04.23. https://pmc.ncbi.nlm.nih.gov/articles/PMC6526269/

  3. Thind AS, Ashford B, Strbenac D, Mitchell J, Lee J, Mueller SA, Minaei E, Perry JR, Ch’ng S, Iyer NG, Clark JR, Gupta R, Ranson M. Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma. Frontiers in Oncology. 2022;12:919118. doi:10.3389/fonc.2022.919118. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.919118/full

  4. Hoogstrate Y, Draaisma K, Ghisai SA, et al. Transcriptome analysis reveals tumor microenvironment changes in glioblastoma. Cancer Cell. 2023;41(4):678-692.e7. doi:10.1016/j.ccell.2023.02.019. https://www.sciencedirect.com/science/article/pii/S1535610823000478

TCGA And Xena Data Sources

The ggoncoplot paper states that its TCGA breast carcinoma examples use TCGA Research Network data, with methylation, expression, and somatic mutation data obtained from the Xena TCGA Pan-Cancer Atlas Hub.

  1. The Cancer Genome Atlas Research Network. The Cancer Genome Atlas Program. National Cancer Institute. https://www.cancer.gov/tcga

  2. Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, Zhu J, Haussler D. Visualizing and interpreting cancer genomics data via the Xena platform. Nature Biotechnology. 2020;38(6):675-678. doi:10.1038/s41587-020-0546-8. https://doi.org/10.1038/s41587-020-0546-8

  3. Ellrott K, Bailey MH, Saksena G, Covington KR, Kandoth C, Stewart C, Hess J, Ma S, Chiotti KE, McLellan M, Sofia HJ, Hutter C, Getz G, Wheeler D, Ding L, MC3 Working Group, The Cancer Genome Atlas Research Network. Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Systems. 2018;6(3):271-281.e7. doi:10.1016/j.cels.2018.03.002. https://doi.org/10.1016/j.cels.2018.03.002

Documentation And Source Image Pages

These are not all primary research papers, but they are the direct public source locations for several imported reference images.

  1. Lee S. fuc documentation: Tutorials. Read the Docs. Revision 7b0fbfbd. Accessed 2026-05-26. https://sbslee-fuc.readthedocs.io/en/latest/tutorials.html

  2. Lee S. fuc-data TCGA-LAML datasets. GitHub. Accessed 2026-05-26. https://github.com/sbslee/fuc-data

  3. El-Kamand S, Quinn JMW, Cowley MJ. ggoncoplot source repository, paper assets, and README figures. GitHub. Accessed 2026-05-26. https://github.com/selkamand/ggoncoplot

  4. El-Kamand S, Quinn JMW, Cowley MJ. Easily Create Interactive Oncoplots: ggoncoplot package site. Accessed 2026-05-26. https://selkamand.github.io/ggoncoplot/

Notes

  • The generated PyOncoplot gallery uses fixture data in python_refactor_goal_sources/syntheitic_goal_data/; most files are deterministic stand-ins, while the Python/fuc AML files are compact derivatives of upstream fuc-data examples.

  • The original numbered goal plots are source/reference images and should stay immutable except when the active goal set is intentionally renumbered.

  • The spelling syntheitic_goal_data matches the existing repository path.