2026-scerevisiae-dataset

Purpose

This repository goes with the pub "Coherent Raman imaging of live, unfixed Saccharomyces cerevisiae and mutants rad6 and aah1", which describes a Raman microscopy dataset acquired on Saccharomyces cerevisiae (budding yeast). The data were acquired using SRS (Stimulated Raman Scattering) and CARS (Coherent Anti-Stokes Raman Scattering) microscopy on a Leica STELLARIS 8 microscope. This repository shares code to open the LIF (Leica Image Format) files, view them, and export images and metadata. It was specifically written for this dataset.

Installation and setup

This repository uses conda to manage software environments and installations. You can find operating system-specific instructions for installing miniconda here. After installing conda and mamba, run the following command to create the pipeline run environment. Follow docs/installation.md for more detailed instructions.

mamba env create -n 2026-scerevisiae --file envs/dev.yml
conda activate 2026-scerevisiae
pip install -e .

Data

The input data are LIF files, available on Zenodo. They contain data from S. cerevisiae BY4741 wild-type and mutant strains rad6∆ and aah1∆ (orthologs of human UBE2A and ADA1, respectively). The outputs are the key metadata (JSON, CSV) and composite images of the data (PNG, SVG). The data should be put into the data/ folder and relative file paths should be updated in the notebooks before running them.

Overview

Description of the folder structure

.
├── data/                          # raw LIF files (on zenodo)
│   ├── 2026-01-15/
│   │   ├── Scerevisiae_BY4741_WT.lif
│   │   └── Scerevisiae_rad6.lif
│   ├── 2026-01-16/
│   │   └── Scerevisiae_aah1.lif
│   └── README.md
├── src/
│   └── stellaris_analysis/        # python utilities
│       ├── __init__.py
│       ├── io_utils.py            # load/export LIF files
│       ├── align_utils.py         # image alignment
│       └── viz_utils.py           # visualization functions
├── scripts/                       # command-line tools
│   ├── list_lif_contents.py       # list LIF contents
│   └── generate_metadata.py       # generate metadata CSV, JSON
├── examples/                      # jupyter notebooks
│   ├── 01_view_lif_contents.ipynb
│   ├── 02_generate_overlay.ipynb
│   └── 03_explore_lambda_scans.ipynb
├── docs/                          # documentation
│   ├── installation.md
│   ├── sample_description.md
│   ├── metadata_schema.md
├── envs/
│   └── dev.yml                    # conda environment specification
├── outputs/                       # all outputs organized by LIF file
│   ├── Scerevisiae_BY4741_WT/     # outputs for BY4741 wild-type
│   │   ├── metadata/              # metadata JSON and CSV
│   │   ├── spectra/               # exported Raman spectra
│   │   ├── movies/                # lambda scan movies
│   │   └── figures/               # RGB overlay figures
│   ├── Scerevisiae_rad6/          # outputs for rad6∆ mutant
│   ├── Scerevisiae_aah1/          # outputs for aah1∆ mutant
│   └── README.md
├── pyproject.toml                 # python package configuration
├── .pre-commit-config.yaml        # pre-commit hooks configuration
├── LICENSE
└── README.md

Methods

This repository contains scripts (scripts/) containing functions for generating metadata and listing LIF contents. Examples of their usage, and the scripts in src/, are provided in the accompanying notebooks (in examples/). For instance, you can:

1. Download data from Zenodo.
2. View LIF contents, explore z-stacks, and export metadata using 'examples/01_view_lif_contents.ipynb'.
3. Generate RGB overlay images using 'examples/02_generate_overlay.ipynb'. These are used in the pub.
4. Explore lambda scans, plot spectra, and export movies using 'examples/03_explore_lambda_scans.ipynb'.

Compute specifications

We used a 2023 MacBook Pro with an Apple M3 Max chip and 36 GB memory.

Contributing

See how we recognize feedback and contributions to our code.

Planned updates

• streamline metadata handling and outputs