Generating Single Cell RNA-seq Data with Diffusion Models
14.06.2024Great progress has been made recently in the field of single-cell analysis. However, there are still some major hurdles, such as generating high-quality data. On the other hand, diffusion models are on everyone's lips, which would be ideal for generating new data. Furthermore, diffusion models might be able to handle biological noise better than conventional models.
Single Cell Analysis is pivotal in today's medical research, yet its effectiveness hinges on the development of robust models capable of deciphering the variability introduced by biological noise. Diffusion models, known for their ability to generate realistic samples by learning a step-by-step denoising, could therefore be adept at capturing and learning this inherent biological noise.
In this work we want to improve an existing model and exchange or modify individual components to achieve better results.
Supervisor: Martin Rackl
