scSimseq: A Nonparametric Simulation Method for Single-Cell RNA Data

With the wide use of new generation sequencing technology, single-cell RNA data has gradually become the mainstream object of research. However, it is costly to obtain single-cell RNA data directly from organisms. Therefore, how to obtain these data simply and quickly is an important problem. In order to meet the needs of comparative experiments, the simulation method of single-cell RNA data usually needs not only the statistics of the simulation data are close to the original data, but also the gene and cell samples that can retain the original data in the simulation data. Here, we introduce a data-based simulation method. On the basis of retaining the gene and cell samples of the original data, we can simulate the single-cell RNA data at low cost and ensure that the simulation results are similar to the original data in most characteristics. Through a large number of numerical experiments, it is proved that the proposed method is superior to other simulation methods in terms of distribution of gene expression.