Deep generative models are a series of unsupervised methods to learn any kind of data distribution and generate new dataset based on the learned distribution. These methods are developed in computer vision community and can be used to generate new creative images. The well known approaches are Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN). The success of these methods has sparkled the interests of the researchers from other domains like bioinformatics, coinciding with the recently development of single-cell technology. In biological domain, single cell technology basically means two things: single cell RNAseq and mass cytometry; regardless of the intention and logics behind the experiment setup, both technology delivers multiple-sample-multiple-dimension datasets, which are the natural match to these generative models, at least to the outward appearances.
I set up such a project to critically investigate and examine the application of those generative models to the single cell datasets. There are multiple focuses: philosophy behind the data analysis and data interpretation, the architecture of the methods, the consistency of the methods (newer ones vs classical ones).
Project duration: December 2018 - January 2020
- Project A: Single-Cell Data Analysis Using MMD Variational Autoencoder
- Project B: ongoing.