DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
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DOI：
10.1186/s13059-019-1837-6
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发表时间：
2019-10-18
期刊：
影响因子：
12.3
通讯作者：
Garmire, Lana X.
中科院分区：
文献类型：
--
作者：
Arisdakessian, Cedric;Poirion, Olivier;Garmire, Lana X.
单细胞RNA测序（scRNA-seq）为同时研究数万个单细胞的基因表达提供了新的机会。我们提出了DeepImpute，这是一种基于深度神经网络的插补算法，它使用dropout层和损失函数来学习数据中的模式，从而实现准确的插补。总体而言，DeepImpute在实验数据上的准确性优于其他六种公开的scRNA-seq插补方法，如均方误差或Pearson相关系数所测量的。DeepImpute是一个准确，快速和可扩展的插补工具，适合处理不断增加的scRNA-seq数据量，并可免费获得。
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson's correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at .