stochprofML：R中使用极大似然估计的随机剖面
stochprofML: Stochastic Profiling Using Maximum Likelihood Estimation in R
摘要：
组织通常在其单细胞分子表达方面是异质的，这可以支配细胞命运的调节。为了理解发育和疾病，重要的是量化给定组织中的异质性。我们介绍了\proglang{R}包\pkg{stochprofML}，它的目的是从小的随机池的细胞的累积表达的异质性参数。这种方法比混合样品的分层更重要，节省了成本和精力，测量误差更小。该方法使用极大似然原理，最初是在Bajikar等人。（2014）; Tirier等人（2019）将其扩展到不同的样本池大小。我们评估算法的性能在模拟研究和提出进一步的应用机会。
英文摘要：
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.
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