Multi-omics integration in the age of million single-cell data
An explosion in single-cell technologies has revealed a previously underappreciated
heterogeneity of cell types and novel cell-state associations with sex, disease, development …
heterogeneity of cell types and novel cell-state associations with sex, disease, development …
Unbalanced optimal transport, from theory to numerics
Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …
in a geometrically faithful way point clouds and more generally probability distributions. The …
Alignment and integration of spatial transcriptomics data
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …
Learning single-cell perturbation responses using neural optimal transport
Understanding and predicting molecular responses in single cells upon chemical, genetic or
mechanical perturbations is a core question in biology. Obtaining single-cell measurements …
mechanical perturbations is a core question in biology. Obtaining single-cell measurements …
Curriculum reinforcement learning via constrained optimal transport
Curriculum reinforcement learning (CRL) allows solving complex tasks by generating a
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …
tailored sequence of learning tasks, starting from easy ones and subsequently increasing …
Bi-order multimodal integration of single-cell data
Integration of single-cell multiomics profiles generated by different single-cell technologies
from the same biological sample is still challenging. Previous approaches based on shared …
from the same biological sample is still challenging. Previous approaches based on shared …
Linear-time gromov wasserstein distances using low rank couplings and costs
The ability to align points across two related yet incomparable point clouds (eg living in
different spaces) plays an important role in machine learning. The Gromov-Wasserstein …
different spaces) plays an important role in machine learning. The Gromov-Wasserstein …
Fused Gromov-Wasserstein distance for structured objects
Optimal transport theory has recently found many applications in machine learning thanks to
its capacity to meaningfully compare various machine learning objects that are viewed as …
its capacity to meaningfully compare various machine learning objects that are viewed as …
Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
Motivation Single-cell multi-omics sequencing data can provide a comprehensive molecular
view of cells. However, effective approaches for the integrative analysis of such data are …
view of cells. However, effective approaches for the integrative analysis of such data are …
Infoot: Information maximizing optimal transport
Optimal transport aligns samples across distributions by minimizing the transportation cost
between them, eg, the geometric distances. Yet, it ignores coherence structure in the data …
between them, eg, the geometric distances. Yet, it ignores coherence structure in the data …