文章基本信息

标题：Synthetic single cell RNA sequencing data from small pilot studies using deep generative models
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作者：Martin Treppner ; Adrián Salas-Bastos ; Moritz Hess 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2021
卷号：11
DOI：10.1038/s41598-021-88875-4
语种：English
出版社：Springer Nature
摘要：Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the \documentclass[12pt