High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing
datasets provide unprecedented opportunities to dissect cell–cell communication (CCC) …

Single-cell RNA sequencing (scRNA-seq) provides details for individual cells; however,
crucial spatial information is often lost. We present SpaOTsc, a method relying on structured …

Optimal transport has recently been reintroduced to the machine learning community thanks
in part to novel efficient optimization procedures allowing for medium to large scale …

Multiplexed RNA sequencing in individual cells is transforming basic and clinical life
sciences,,–. Often, however, tissues must first be dissociated, and crucial information about …

Single-cell genomic technologies enable the multimodal profiling of millions of cells across
temporal and spatial dimensions. However, experimental limitations hinder the …

Optimal transport maps between two probability distributions $\mu $ and $\nu $ on $\R^ d $
have found extensive applications in both machine learning and statistics. In practice, these …

We consider the problem of estimating the optimal transport map between two probability
distributions, $ P $ and $ Q $ in $\mathbb {R}^ d $, on the basis of iid samples. All existing …

The supplementary materials contain more background on convex functions, wavelets and
empirical processes, as well as tools to prove lower bounds, alternative assumptions based …