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visnormsc: A Graphical User Interface to Normalize Single-cell RNA Sequencing Data

机译:VisnorMSC:一种标准化单个单元RNA测序数据的图形用户界面

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摘要

Single-cell RNA sequencing (RNA-seq) allows the analysis of gene expression with high resolution. The intrinsic defects of this promising technology imports technical noise into the single-cell RNA-seq data, increasing the difficulty of accurate downstream inference. Normalization is a crucial step in single-cell RNA-seq data pre-processing. SCnorm is an accurate and efficient method that can be used for this purpose. An R implementation of this method is currently available. On one hand, the R package possesses many excellent features from R. On the other hand, R programming ability is required, which prevents the biologists who lack the skills from learning to use it quickly. To make this method more user-friendly, we developed a graphical user interface, visnormsc, for normalization of single-cell RNA-seq data. It is implemented in Python and is freely available at https://github.com/solo7773/visnormsc. Although visnormsc is based on the existing method, it contributes to this field by offering a user-friendly alternative. The out-of-the-box and cross-platform features make visnormsc easy to learn and to use. It is expected to serve biologists by simplifying single-cell RNA-seq normalization.
机译:单细胞RNA测序(RNA-SEQ)允许以高分辨率分析基因表达。本有前途技术的内在缺陷将技术噪声导入单细胞RNA-SEQ数据,增加了准确下游推理的难度。归一化是单细胞RNA-SEQ数据预处理中的关键步骤。 SCNORM是一种准确而有效的方法,可用于此目的。目前可以使用此方法的R实现。一方面,R包具有来自R的许多优异的特征。另一方面,R编程能力是必需的,这可以防止生物学家缺乏学习迅速使用技能的生物学家。为了使这种方法更加用户友好,我们开发了一个图形用户界面VisnormSC,用于单小区RNA-SEQ数据的归一化。它是在Python中实现的,并在https://github.com/solo7773/visnormsc上自由使用。虽然VisnorMSC基于现有方法,但它通过提供用户友好的替代方案来贡献该领域。开箱即用和跨平台功能使VisnorMSC易于学习和使用。预计通过简化单细胞RNA-SEQ标准化来服务生物学家。

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