Report
Multimodal single cell data integration challenge: results and lessons learned
| العنوان: | Multimodal single cell data integration challenge: results and lessons learned |
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| المؤلفون: | Lance, Christopher, Luecken, Malte D., Burkhardt, Daniel B., Cannoodt, Robrecht, Rautenstrauch, Pia, Laddach, Anna, Ubingazhibov, Aidyn, Cao, Zhi-Jie, Deng, Kaiwen, Khan, Sumeer Ahmad, Liu, Qiao, Russkikh, Nikolay, Ryazantsev, Gleb, Ohler, Uwe, Pisco, Angela Oliveira, Bloom, Jonathan, Krishnaswamy, Smita, Theis, Fabian J., NeurIPS 2021 Multimodal data integration competition participants |
| المساهمون: | Biological and Environmental Science and Engineering (BESE) Division |
| بيانات النشر: | Cold Spring Harbor Laboratory |
| سنة النشر: | 2022 |
| المجموعة: | King Abdullah University of Science and Technology: KAUST Repository |
| الوصف: | Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges arising from sparsity, technical and biological variability, and high dimensionality of the data hinder the derivation of such mechanistic insights. To promote the innovation of algorithms for analysis of multimodal single-cell data, we organized a competition at NeurIPS 2021 applying the Common Task Framework to multimodal single-cell data integration. For this competition we generated the first multimodal benchmarking dataset for single-cell biology and defined three tasks in this domain: prediction of missing modalities, aligning modalities, and learning a joint representation across modalities. We further specified evaluation metrics and developed a cloud-based algorithm evaluation pipeline. Using this setup, 280 competitors submitted over 2600 proposed solutions within a 3 month period, showcasing substantial innovation especially in the modality alignment task. Here, we present the results, describe trends of well performing approaches, and discuss challenges associated with running the competition. ; Thanks to all participants, organizers, and people generating and analyzing the data. We thank Cellarity, Saturncloud, and Biolegend for sponsorship of the competition . This project has been made possible in part by grant number 2021- 235155 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation and by the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [ZT-I-PF-5- 01] and sparse2big [ZT-I-0007]. CL is supported by the joint research school Munich School for Data Science (MUDS). PR and UO acknowledge support by DFG Research Unit FOR 2841 and the DFG International Research Training Group IRTG 2403. |
| نوع الوثيقة: | report |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | Lance, C., Luecken, M. D., Burkhardt, D. B., Cannoodt, R., Rautenstrauch, P., Laddach, A., Ubingazhibov, A., Cao, Z.-J., Deng, K., Khan, S., Liu, Q., Russkikh, N., Ryazantsev, G., Ohler, U., Pisco, A. O., Bloom, J., Krishnaswamy, S., & Theis, F. J. (2022). Multimodal single cell data integration challenge: results and lessons learned. https://doi.org/10.1101/2022.04.11.487796; http://hdl.handle.net/10754/686304 |
| DOI: | 10.1101/2022.04.11.487796 |
| الاتاحة: | http://hdl.handle.net/10754/686304 https://doi.org/10.1101/2022.04.11.487796 |
| Rights: | This is a preprint version of a paper and has not been peer reviewed. Archived with thanks to Cold Spring Harbor Laboratory.under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ ; http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| رقم الانضمام: | edsbas.4E2457DE |
| قاعدة البيانات: | BASE |
| DOI: | 10.1101/2022.04.11.487796 |
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