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---
title: "Outcomes from EuroBioC2026 Tidyomics Hackathon"
author: "Juan Henao"
date: "2026-07-20"
package: tidyomics
tags:
- tidyomics/tidyomicsBlog
- hackathon
- tidyverse
- bioconductor
description: "Maintaining and refining the Tidyomics ecosystem: enhancing core packages and interoperability for EuroBioC2026"
image: thumbnail.jpg
format:
html:
toc: true
toc-float: true
execute:
freeze: true
---

# Two days, four participants, four aims

The first `Tidyomics` community hackathon took place on 1st and 2nd June in Turku, Finland, as part of the **EuroBioC2026** conference pre-events. Here, four researchers from around the globe joined efforts to extend the capabilities of tidy operations for omics data, focusing on solving current bugs.

![Participants of the first Tidyomics community hackathon (from left to right): Jasper Spitzer, Carissa Chen, Marco Geigges, Stevie Pederson](participants.jpg)

Concretely, the hackathon focused on four key aspects:

1. Development
2. Bugs
3. Enhancements
4. Learning

![Schematic representation of the Tidyomics hackathon aims](nanobanana2.png)

# What's new?

The advances produced during this hackathon are available and described in detail in the [BioHackrXiv](https://osf.io/preprints/biohackrxiv/cd9s6_v1) publication. In summary:

1. `tidyAnnData` has been introduced as a new package to provide tidy operations for AnnData objects generated by the `anndataR` package
2. Information access to core `Tidyomics` packages was inconsistent and some links were broken in the main GitHub page; taking advantage of this hackathon, access to correct information was restored
3. The `DFplyr` package was enhanced by improving current methods (e.g. `GroupedDataFrame` and `count.DataFrame`) and making them suitable for universal column names
4. The `tidybulk` package was enhanced to adjust the `lfcShrink()` function to use the different available methods (`apeglm` and `ashr`) and to generate a reduced dimensionality-based plot (PCA) for visual inspection of e.g. batch effects and outlier detection
5. A standardised vignette for `tidySingleCellExperiment` has been developed to provide an extensive and concise guide for new users, encompassing both examples comparable to base R code and best practices on single-cell analysis using tidy operations

# What's next?

Five major contributions in two productive days is all a win!

Beyond this wonderful experience, there is work to do: finalising the development of `tidyAnnData` to make it publicly available, fixing additional bugs and enhancing current packages, and standardising the rest of the `Tidyomics` packages' vignettes. These efforts open the doors for future events and continuing open-science work.

We look forward to seeing you at the coming `Tidyomics` hackathon events to speed up software development, bug fixing, and new material development from [open problems in Tidyomics](https://github.com/orgs/tidyomics/projects/1), or to work on your own ideas for providing tidy operations in omics data analysis.
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