MINDS Seminar on Data Scienceㅣ Towards realistic synthetic single-cell RNA sequencing generation with deep learning

Single-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps, or identifying rare subpopulations. However, a critical challenge in this space is the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation, or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this talk, I will provide a brief overview of deep learning methods for generating realistic synthetic scRNAseq data, and present on ACTIVA: a novel framework for generating synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies.

https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09

ID : 688 896 1076 / PW : 54321