ABSTRACT

The high-resolution feature of single-cell transcriptome sequencing technology allows researchers to observe cellular gene expression profiles at the single-cell level, offering numerous possibilities for subsequent biomedical investigation. However, the unavoidable technical impact of high missing values in the gene-cell expression matrices generated by insufficient RNA input severely hampers the accuracy of downstream analysis. To address this problem, it is essential to develop a more rapid and stable imputation method with greater accuracy, which should not only be able to recover the missing data, but also effectively facilitate the following biological mechanism analysis. The existing imputation methods all have their drawbacks and limitations, some require pre-assumed data distribution, some cannot distinguish between technical and biological zeros, and some have poor computational performance. In this paper, we presented a novel imputation software FRMC for single-cell RNA-Seq data, which innovates a fast and accurate singular value thresholding approximation method. The experiments demonstrated that FRMC can not only precisely distinguish ‘true zeros’ from dropout events and correctly impute missing values attributed to technical noises, but also effectively enhance intracellular and intergenic connections and achieve accurate clustering of cells in biological applications. In summary, FRMC can be a powerful tool for analysing single-cell data because it ensures biological significance, accuracy, and rapidity simultaneously. FRMC is implemented in Python and is freely accessible to non-commercial users on GitHub: https://github.com/HUST-DataMan/FRMC.

摘要

单细胞转录组测序技术的高分辨率特性使研究人员能够在单细胞水平观察细胞基因表达谱，为后续的生物医学研究提供了多种可能性。然而，由于 RNA 输入不足而产生的基因细胞表达矩阵中的高缺失值不可避免地会产生技术影响，严重阻碍了下游分析的准确性。为了解决这个问题，需要开发一种更快速、更稳定、精度更高的插补方法，不仅要能够恢复缺失的数据，还要有效地促进后续的生物学机制分析。现有的插补方法都有其缺点和局限性，有些需要预先假设的数据分布，有些无法区分技术零和生物零，有些计算性能较差。在本文中，我们提出了一种用于单细胞 RNA-Seq 数据的新型插补软件 FRMC，它创新了一种快速准确的奇异值阈值逼近方法。实验表明，FRMC不仅可以准确地区分“真零”和辍学事件，并正确估算归因于技术噪声的缺失值，还可以有效地增强细胞内和基因间的连接，并在生物学应用中实现细胞的准确聚类。总之，FRMC 可以成为分析单细胞数据的强大工具，因为它同时确保了生物学意义、准确性和快速性。FRMC 是用 Python 实现的，非商业用户可以在 GitHub 上免费访问：https：