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. 2024 Mar 5;25(1):98.
doi: 10.1186/s12859-024-05721-w.

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

David L Hölscher  1   2 , Michael Goedertier  1   3 , Barbara M Klinkhammer  1 , Patrick Droste  1   2 , Ivan G Costa  3 , Peter Boor #  1   2 , Roman D Bülow #  4
Affiliations

tRigon: an R package and Shiny App for integrative (path-)omics data analysis

David L Hölscher et al. BMC Bioinformatics. .

Abstract

Background: Pathomics facilitates automated, reproducible and precise histopathology analysis and morphological phenotyping. Similar to molecular omics, pathomics datasets are high-dimensional, but also face large outlier variability and inherent data missingness, making quick and comprehensible data analysis challenging. To facilitate pathomics data analysis and interpretation as well as support a broad implementation we developed tRigon (Toolbox foR InteGrative (path-)Omics data aNalysis), a Shiny application for fast, comprehensive and reproducible pathomics analysis.

Results: tRigon is available via the CRAN repository ( https://cran.r-project.org/web/packages/tRigon ) with its source code available on GitLab ( https://git-ce.rwth-aachen.de/labooratory-ai/trigon ). The tRigon package can be installed locally and its application can be executed from the R console via the command 'tRigon::run_tRigon()'. Alternatively, the application is hosted online and can be accessed at https://labooratory.shinyapps.io/tRigon . We show fast computation of small, medium and large datasets in a low- and high-performance hardware setting, indicating broad applicability of tRigon.

Conclusions: tRigon allows researchers without coding abilities to perform exploratory feature analyses of pathomics and non-pathomics datasets on their own using a variety of hardware.

Keywords: Data exploration; Pathomics; Statistics; User interface.

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Conflict of interest statement

The authors declare they have no competing interests.

Figures

Fig. 1
Fig. 1
Overview of the available tRigon functions with their respective appearance in the user interface (ui)
Fig. 2
Fig. 2
User interfaces of the a load/process data and b descriptive statistics tabs
Fig. 3
Fig. 3
User interface of the a plotting tab. b example box plot and c example ridgeline plot with logarithmic scale set to “on”
Fig. 4
Fig. 4
User interface of the a descriptive statistics tab and b example output for the 100-times bootstrapped comparisons of medians with 95% confidence intervals for the feature “glom_tuft_shape_circularity” stratified by histopathological diagnoses in the AC_B cohort. Additional selectable tests include pairwise Wilcoxon-rank test and Kruskal–Wallis test
Fig. 5
Fig. 5
User interface of the a clustering tab. Features to be clustered can be selected, as well as the number of clusters and whether data points should be assigned to a group based on a grouping column in the metadata
Fig. 6
Fig. 6
User interface of the a feature Importance tab. Features can be selected to perform random forest- or recursive feature-based importance analysis for classification and regression tasks. b Example feature importance plots showing mean decrease accuracy and mean decrease gini for the selected features and dependent variable
Fig. 7
Fig. 7
User interface of the a correlation tab. Features can be selected to perform single- or multiple correlation showing a single correlation plot as an example output. b Example multiple correlation visualized as a correlation matrix
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