论文标题
StochProfMl：使用最大似然估计的随机分析
stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R
论文作者
论文摘要
组织在单细胞分子表达中通常是异质的，这可以控制细胞命运的调节。为了理解发育和疾病，量化给定组织中的异质性很重要。我们介绍了\ proglang {r} package \ pkg {stochprofml}，该{stochprofml}旨在从小细胞的累积表达中参数化异质性。这种方法超过了混合样品的混合，以节省成本和精力和更少的测量错误。该方法使用最大似然原理，最初是在Bajikar等人（2014年）中提出的。 Tirier等人使用了其扩展到不同的池尺寸。 （2019）。我们在模拟研究中评估了该算法的性能，并提供了进一步的应用机会。
Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. We introduce the \proglang{R} package \pkg{stochprofML} which is designed to parameterize heterogeneity from the cumulative expression of small random pools of cells. This method outweighs the demixing of mixed samples with a saving in cost and effort and less measurement error. The approach uses the maximum likelihood principle and was originally presented in Bajikar et al.(2014); its extension to varying pool sizes was used in Tirier et al. (2019). We evaluate the algorithm's performance in simulation studies and present further application opportunities.
