ArticlebioRxiv : the preprint server for biology2025
Debiasing Sinkhorn divergence in optimal transport of cellular dynamics.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell RNA-seq analysis characterizes developmental mechanisms of cellular differentiation, lineage determination, and reprogramming with differential conditioning of the microenvironment. In this article, the underlying dynamics are formulated via optimal transport with algorithms that calculate the transition probability of the state of cell dynamics over time. The algorithmic biases of optimal transport (OT) due to entropic regularization are balanced by Sinkhorn divergence, which normally de-biases the regularized transport by centering them. In the case of reprogramming mouse embryonic fibroblasts [1] with dense time points, Sinkhorn divergence is shown to improve the trajectories of targeted cell fates depending on the specific cell types. When the time points are filtered out with sparser 9 and 5 time points, some cell phenotypes show better outcomes from strong entropic regularization. For 9 time points with 2 days interval, Sinkhorn divergence shows a clear advantage with broad bandwith of optimal entropic regularization. For these derived time points, when the cell population is scaled down from
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.