Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstrasse 1, 85764, Neuherberg, Germany.
Department of Mathematics, Technical University Munich, Boltzmannstrasse 3, 85748, Garching, Germany.
BMC Bioinformatics. 2021 Mar 15;22(1):123. doi: 10.1186/s12859-021-03970-7.
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 present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm's performance in simulation studies and present further application opportunities.
Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.
组织在单细胞分子表达上往往存在异质性，而这种异质性可以控制细胞命运的调控。为了理解发育和疾病，量化给定组织中的异质性非常重要。
我们提出了 R 包 stochprofML，它使用最大似然原理从小的随机细胞池的累积表达中对异质性进行参数化。我们在模拟研究中评估了该算法的性能，并提出了进一步的应用机会。
随机分析比用混合样本进行必要的解混更有优势，可以节省实验成本和工作量，减少测量误差。它为参数化异质性、估计潜在的池组成以及检测样本间细胞群体之间的差异提供了可能性。
