Generating Data using Monte Carlo Dropout For many analytical problems the challenge is to handle huge amounts of available data. However, there are data science application areas where collecting information is difficult and costly, e.g., in the study of geological phenomena, rare diseases, faults in complex systems, insurance frauds, etc. In many such cases, generators of synthetic data with the same statistical and predictive properties as the actual data allow efficient simulations and development of tools and applications. In this work, we propose the incorporation of Monte Carlo Dropout method within Autoencoder (MCDAE) and Variational Autoencoder (MCDVAE) as efficient generators of synthetic data sets. As the Variational Autoencoder (VAE) is one of the most popular generator techniques, we explore its similarities and differences to the proposed methods. We compare the generated data sets with the original data based on statistical properties, structural similarity, and predictive similarity. The results obtained show a strong similarity between the results of VAE, MCDVAE and MCDAE; however, the proposed methods are faster and can generate values similar to specific selected initial instances.

Deep generative autoencoder for lowdimensional embeding extraction from singlecell RNAseq data Singlecell RNA sequencing (scRNAseq) can reveal biological diversity at the cellular level that are unexplored by bulk RNA sequencing (RNAseq), but they suffer from the excessive zero expression counts and the limitation of the scalability in practice. Here, we propose a nonlinear generative autoencoder based method, scSVA, relying on an integration of variational autoencoder and dropout imputations. Specifically, scSVA automatically identifies the dropouts and recovery these values only to avoid introducing new biases. Then, scSVA utilizes stochastic optimization and deep neural network to extract the lowdimensional embedding from gene expression levels. We illustrate the benefits of scSVA through indepth real analyses of six published scRNAseq data sets. scSVA is up to 1.3 times more powerful cell clustering accuracy than existing approaches. The high power of scSVA allows us to identify new cell types that reveal new biology from scRNAseq data that otherwise cannot be revealed by existing approaches.