Vallejos Catalina A, Richardson Sylvia, Marioni John C
MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK.
EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK.
Genome Biol. 2016 Apr 15;17:70. doi: 10.1186/s13059-016-0930-3.
Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method's performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.
传统的差异表达工具仅限于检测整体表达的变化，无法揭示单细胞水平数据集所提供的丰富信息。我们提出了一种贝叶斯层次模型，该模型基于BASiCS构建，用于研究均值比较之外的变化，纳入了内置归一化，并通过从加标基因中借用信息来量化技术假象。使用概率方法，我们突出显示了细胞间异质性发生变化但其整体表达保持不变的基因。对照实验验证了我们方法的性能，一个案例研究表明可以揭示新的生物学见解。我们的方法用R实现，可在https://github.com/catavallejos/BASiCS获取。
