Optimal Transport using GANs for Lineage Tracing

07/23/2020
by   Neha Prasad, et al.
0

In this paper, we present Super-OT, a novel approach to computational lineage tracing that combines a supervised learning framework with optimal transport based on Generative Adversarial Networks (GANs). Unlike previous approaches to lineage tracing, Super-OT has the flexibility to integrate paired data. We benchmark Super-OT based on single-cell RNA-seq data against Waddington-OT, a popular approach for lineage tracing that also employs optimal transport. We show that Super-OT achieves gains over Waddington-OT in predicting the class outcome of cells during differentiation, since it allows the integration of additional information during

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/26/2018

Scalable Unbalanced Optimal Transport using Generative Adversarial Networks

Generative adversarial networks (GANs) are an expressive class of neural...
research
10/16/2019

Optimal Transport Based Generative Autoencoders

The field of deep generative modeling is dominated by generative adversa...
research
02/02/2022

Unpaired Image Super-Resolution with Optimal Transport Maps

Real-world image super-resolution (SR) tasks often do not have paired da...
research
07/09/2019

k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport

Generative adversarial networks (GANs) are the state of the art in gener...
research
01/14/2021

Convex Smoothed Autoencoder-Optimal Transport model

Generative modelling is a key tool in unsupervised machine learning whic...
research
02/10/2020

Connecting GANs and MFGs

Generative Adversarial Networks (GANs), introduced in 2014 [12], have ce...
research
02/20/2019

Dynamic Cell Imaging in PET with Optimal Transport Regularization

We propose a novel dynamic image reconstruction method from PET listmode...

Please sign up or login with your details

Forgot password? Click here to reset