GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.
GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership.
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DOI：
10.1186/s13059-023-03067-9
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发表时间：
2023-10-19
期刊：
影响因子：
12.3
通讯作者：
Stephens, Matthew
中科院分区：
文献类型：
--
作者：
Carbonetto, Peter;Luo, Kaixuan;Sarkar, Abhishek;Hung, Anthony;Tayeb, Karl;Pott, Sebastian;Stephens, Matthew
关键词：
基于部件的表示，例如非负矩阵分解和主题建模，已经被用于从单细胞测序数据集中识别结构，特别是没有被聚类或其他降维方法很好地捕获的结构。然而，解释各个部分仍然是一个挑战。为了解决这一挑战，我们扩展了差异表达分析的方法，允许细胞具有多个组的部分成员资格。我们称这种隶属度为差异表达(GoM DE)。我们说明了GoM DE在注释几个单细胞RNA-seq和ATAC-seq数据集中确定的主题方面的好处。网上版载有补充材料，可在10.1186/s13059-023-03067-9查阅。
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets. The online version contains supplementary material available at 10.1186/s13059-023-03067-9.
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