Tree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell types

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  1. Department of Statistics
  2. Tree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell types

Tree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell types

TitleTree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell types
Publication TypeUnpublished
Year of Publication2023
AuthorsYe, PE, Zhang, Y, Geltink, RIKlein, Park, YP
Series TitlebioRxiv
Pagination2023.11.06.565879
AbstractImmune cells undergo a series of differentiation steps following a lineage-tree structure stemming from hematopoietic stem cells. During differentiation of immune cells in both homeostasis and pathological processes, many gene regulatory mechanisms are shared by fully differentiated immune cell sub-types. In order to characterize these features quantitatively, we propose LaRCH, a tree-structured embedded topic model. In this model, single-cell gene expression profiles are represented by a mixture of topics consisting of latent features that follow an underlying tree structure, mirroring that of cellular differentiation–nested cluster structures. We present findings of our model trained on simulated single-cell RNA sequencing (scRNA-seq) based on cell-sorted bulk RNA-seq data as well as on a scRNA-seq dataset of over 1.2 million cells from healthy individuals and individuals diagnosed with systemic lupus erythematosus (SLE). The cellular topic profiles estimated by our model markedly improve clustering accuracy over traditional latent variable models and illustrate transcriptomic differences between SLE phenotypes, revealing a pivotal role of multiple immune cell types in disease progression and relapse. Ultimately, LaRCH captures the hierarchical context between cellular subtypes by simultaneously identifying shared and distinct latent features amongst subsets of heterogeneous samples of cells. \#\#\# Competing Interest Statement The authors have declared no competing interest.