Carbonetto Peter, Luo Kaixuan, Sarkar Abhishek, Hung Anthony, Tayeb Karl, Pott Sebastian, Stephens Matthew
Department of Human Genetics, University of Chicago, Chicago, IL, USA.
Research Computing Center, University of Chicago, Chicago, IL, USA.
bioRxiv. 2023 Sep 14:2023.03.03.531029. doi: 10.1101/2023.03.03.531029.
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.
基于部分的表示方法，如非负矩阵分解和主题建模，已被用于从单细胞测序数据集中识别结构，特别是那些聚类或其他降维方法未能很好捕捉到的结构。然而，解释这些个体部分仍然是一个挑战。为了应对这一挑战，我们通过允许细胞部分隶属于多个组来扩展差异表达分析方法。我们将这种隶属度称为差异表达等级（GoM DE）。我们展示了GoM DE在注释几个单细胞RNA测序和ATAC测序数据集中识别出的主题方面的优势。
