基于迁移学习的单细胞转录组学数据去噪。
Data denoising with transfer learning in single-cell transcriptomics.
机构信息
Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA.
Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA.
出版信息
Nat Methods. 2019 Sep;16(9):875-878. doi: 10.1038/s41592-019-0537-1. Epub 2019 Aug 30.
Abstract
Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.
摘要
单细胞 RNA 测序 (scRNA-seq) 数据存在较多噪声和稀疏性。在此，我们表明跨数据集的迁移学习可以显著改善数据质量。通过将深度自动编码器与贝叶斯模型相结合，SAVER-X 可以提取不同实验室、不同条件和不同物种的数据之间可转移的基因-基因关系，从而对新的目标数据集进行降噪。
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