Principal Curvatures Estimation with Applications to Single Cell Data
BP2-STS
- Zhang, Yanlei Université de Montréal, Departement of Mathematics and Statistics
- Mezrag, Lydia Université de Montréal, Departement of Mathematics and Statistics
- Sun, Xingzhi Yale University, Departement of Computer Science
- Xu, Charles Yale University, Departement of Computer Science
- Macdonald, Kincaid Yale University, Departement of Computer Science
- Bhaskar, Dhananjay Yale University, Departement of Computer Science
- Krishnaswamy, Smita Mila – Quebec AI Institute, Montréal, CA
- Wolf, Guy Université de Montréal, Departement of Mathematics and Statistics
- Rieck, Bastian ORCID Université de Fribourg, Department of Informatics, Fribourg, CH
- 2025
Published in:
- ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - IEEE. - 2025, p. 1-5
English
The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq) presents challenges for data analysis due to its massive datasets. A common method in manifold learning consists in hypothesizing that datasets lie on a lower dimensional manifold. This allows to study the geometry of point clouds by extracting meaningful descriptors like curvature. In this work, we will present Adaptive Local PCA (AdaL-PCA), a data-driven method for accurately estimating various notions of intrinsic curvature on data manifolds, in particular principal curvatures for surfaces. The model relies on local PCA to estimate the tangent spaces. The evaluation of AdaL-PCA on sampled surfaces shows state-of-the-art results. Combined with a PHATE embedding, the model applied to single-cell RNA sequencing data allows us to identify key variations in the cellular differentiation.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département d'Informatique
- Language
-
- English
- Classification
- Computer science and technology
- License
- Rights reserved
- Open access status
- green
- Identifiers
- Persistent URL
- https://folia.unifr.ch/unifr/documents/332243
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