BIRDccNEST: Interpretable single cell characterization with inferred directed cell networks

Gizem Cicekli, Adrita Samanta, Hao Zhu, Donna Slonim
Proceedings of the 20th Machine Learning in Computational Biology meeting, PMLR 311:270-279, 2025.

Abstract

We introduce BIRDccNEST (pronounced "bird’s nest"), an efficient unsupervised framework for characterizing cells and defining trajectories in single cell RNA-sequencing data by inferring directed cell-cell relationship networks. These networks are then transformed into cluster flow networks describing directed relationships between cell-cell communities, naturally capturing an interpretable trajectory and characterizing subgroups of cells. We demonstrate that this approach finds interpretable and more coherent cell communities and trajectories on several data sets. Code is available at: https://bcb.cs.tufts.edu/BIRDccNEST.html

Cite this Paper

BibTeX
@InProceedings{pmlr-v311-cicekli25a, title = {BIRDccNEST: Interpretable single cell characterization with inferred directed cell networks}, author = {Cicekli, Gizem and Samanta, Adrita and Zhu, Hao and Slonim, Donna}, booktitle = {Proceedings of the 20th Machine Learning in Computational Biology meeting}, pages = {270--279}, year = {2025}, editor = {Knowles, David A and Koo, Peter K}, volume = {311}, series = {Proceedings of Machine Learning Research}, month = {10--11 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v311/main/assets/cicekli25a/cicekli25a.pdf}, url = {https://proceedings.mlr.press/v311/cicekli25a.html}, abstract = {We introduce BIRDccNEST (pronounced "bird’s nest"), an efficient unsupervised framework for characterizing cells and defining trajectories in single cell RNA-sequencing data by inferring directed cell-cell relationship networks. These networks are then transformed into cluster flow networks describing directed relationships between cell-cell communities, naturally capturing an interpretable trajectory and characterizing subgroups of cells. We demonstrate that this approach finds interpretable and more coherent cell communities and trajectories on several data sets. Code is available at: https://bcb.cs.tufts.edu/BIRDccNEST.html} }
Endnote
%0 Conference Paper %T BIRDccNEST: Interpretable single cell characterization with inferred directed cell networks %A Gizem Cicekli %A Adrita Samanta %A Hao Zhu %A Donna Slonim %B Proceedings of the 20th Machine Learning in Computational Biology meeting %C Proceedings of Machine Learning Research %D 2025 %E David A Knowles %E Peter K Koo %F pmlr-v311-cicekli25a %I PMLR %P 270--279 %U https://proceedings.mlr.press/v311/cicekli25a.html %V 311 %X We introduce BIRDccNEST (pronounced "bird’s nest"), an efficient unsupervised framework for characterizing cells and defining trajectories in single cell RNA-sequencing data by inferring directed cell-cell relationship networks. These networks are then transformed into cluster flow networks describing directed relationships between cell-cell communities, naturally capturing an interpretable trajectory and characterizing subgroups of cells. We demonstrate that this approach finds interpretable and more coherent cell communities and trajectories on several data sets. Code is available at: https://bcb.cs.tufts.edu/BIRDccNEST.html
APA
Cicekli, G., Samanta, A., Zhu, H. & Slonim, D.. (2025). BIRDccNEST: Interpretable single cell characterization with inferred directed cell networks. Proceedings of the 20th Machine Learning in Computational Biology meeting, in Proceedings of Machine Learning Research 311:270-279 Available from https://proceedings.mlr.press/v311/cicekli25a.html.

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