Welcome to the open-source GitHub organization hosted by The Way Lab.
We are a team of scientists and engineers developing methods and software for analyzing microscopy images of cells. Our mission is to reduce human suffering.
Our website contains complete details of our research projects, team members, and funding: https://www.waysciencelab.com/
Check out a few of our open-source projects!
Tomkinson, J., Kern, R., Mattson, C. et al. Toward generalizable phenotype prediction from single-cell morphology representations. BMC Methods 1, 17 (2024). doi: https://doi.org/10.1186/s44330-024-00014-3; GitHub Repository: https://github.com/WayScience/phenotypic_profiling/
Lippincott, M.J., Tomkinson, J., Bunten, D. et al. A morphology and secretome map of pyroptosis. bioRxiv 2024.04.26.591386; doi: https://doi.org/10.1101/2024.04.26.591386; GitHub Repository: https://github.com/WayScience/pyroptosis_signature_data_analysis
Serrano, E., Chandrasekaran, S.N., Bunten, D. et al. Reproducible image-based profiling with Pycytominer. Nat Methods (2025). doi: https://doi.org/10.1038/s41592-025-02611-8; GitHub Repository: https://github.com/cytomining/pycytominer
- coSMicQC: https://github.com/WayScience/coSMicQC
- Pycytominer: https://github.com/cytomining/pycytominer
- CytoTable: https://github.com/cytomining/cytotable
- CytoDataFrame: https://github.com/WayScience/CytoDataFrame
- 3D Organoid Profiling: https://github.com/WayScience/NF1_3D_organoid_profiling_pipeline
- nViz: https://github.com/WayScience/nViz
- virtual_stain_flow: https://github.com/WayScience/virtual_stain_flow
We are leading multidisciplinary scientists in a variety of team-science initiatives.
We are developing a reference Cell Painting data set of pediatric cancer cell lines, and performing several high-throughput, high-content phenotypic drug screens.
- Image analysis and optimization: https://github.com/WayScience/pediatric_cancer_atlas_profiling
We are developing a 3D image analysis pipeline to profile patient-derived organoids from neurofibromatosis type 1 (NF1) patients, and we will be performing several high-throughput, high-content phenotypic drug screens.
- 3D image analysis pipeline: https://github.com/WayScience/NF1_3D_organoid_profiling_pipeline
We are identifying fundamental differences between cardiac fibroblasts from patients with and without heart failure, and we are developing a high-throughput, high-content phenotypic drug screen to identify new therapeutics for cardiac fibrosis.
- Image analysis and machine learning: https://github.com/WayScience/cellpainting_predicts_cardiac_fibrosis
