Single-cell RNA-seq has inspired new discoveries and innovation in the field of developmental and cell biology for the past few years and is useful for studying cellular responses at individual cell resolution. But, due to the paucity of starting RNA, the data acquired have dropouts. To address this, we propose a deep matrix factorization-based method, deepMc, to impute missing values in gene expression data. For the deep architecture of our approach, we draw our motivation from great success of deep learning in solving various machine learning problems. In this study, we support our method with positive results on several evaluation metrics such as clustering of cell populations, differential expression analysis, and cell type separability.

中文翻译：

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deepMc：用于单细胞 RNA-seq 数据插补的深度矩阵完成。

在过去几年中，单细胞 RNA-seq 激发了发育和细胞生物学领域的新发现和创新，可用于以单个细胞分辨率研究细胞反应。但是，由于缺乏起始 RNA，获得的数据有丢失。为了解决这个问题，我们提出了一种基于深度矩阵分解的方法 deepMc 来估算基因表达数据中的缺失值。对于我们方法的深度架构，我们从深度学习在解决各种机器学习问题方面取得的巨大成功中汲取了动力。在这项研究中，我们支持我们的方法，在几个评估指标上取得了积极的结果，例如细胞群的聚类、差异表达分析和细胞类型可分离性。