Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

Abstract

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA

“…Several computational methodologies focused on multicellular integration have emerged: DIALOGUE 40 was developed to systematically uncover combinations of coordinated cellular programs in different cell types from either spatial data or scRNA-seq data; Tensor-cell2cell 41 can reveal context-dependent communication patterns linked to various phenotypic states, influenced by distinct combinations of cell types and ligand-receptor pairs; MOFAcellulaR 16 allows the integration of measurements of independent single-cell, spatial, and bulk datasets to contextualize multicellular responses in disease. Inspired by the aforementioned methods, especially MOFAcellulaR 16 , we reutilized MOFA framework in scPAFA for multicellular integration. scPAFA can be regarded as a complement to existing pathway analysis methods in scRNA-seq data, aiming to identify interpretable pathway-based multicellular transcriptomic features associated with the biological conditions of interest.…”

“…MOFAcellulaR 16 allows the integration of measurements of independent single-cell, spatial, and bulk datasets to contextualize multicellular responses in disease. Inspired by the aforementioned methods, especially MOFAcellulaR 16 , we reutilized MOFA framework in scPAFA for multicellular integration.…”

“…For instance, we also employed 3CA metaprogram27 on CRC dataset for interpretation based on prior knowledge in oncology.Cell type annotation is a key step in the analysis of scRNA-seq data, thus integrating transcriptomic features across different cell types to facilitate interpretation at the level of biological conditions is an issue worthy of attention. Several computational methodologies focused on multicellular integration have emerged: DIALOGUE40 was developed to systematically uncover combinations of coordinated cellular programs in different cell types from either spatial data or scRNA-seq data; Tensor-cell2cell41 can reveal context-dependent communication patterns linked to various phenotypic states, influenced by distinct combinations of cell types and ligand-receptor pairs; MOFAcellulaR16 allows the integration of measurements of independent single-cell, spatial, and bulk datasets to contextualize multicellular responses in disease. Inspired by the aforementioned methods, especially MOFAcellulaR16 , we reutilized MOFA framework in scPAFA for multicellular integration.…”

“…A typical primary step in the analysis of scRNA-seq data is to partition the cells into clusters and annotate as cell types 15 ; therefore, downstream analysis commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes 16 .…”

“…Gaining advantages from the high flexibility of statistical framework, MOFA was widely applied in different biological contexts, for instance, revealing an axis of heterogeneity associated with the disease outcome in chronic lymphocytic leukemia 19 ; identification of alterations in Alzheimer’s disease 20 ; uncovering genotypic, microbiome, and metabolomic factors associated with adenoma and colorectal cancer risk 21 . Inspired by Ramirez et al 16 , we reutilized MOFA to identify multicellular pathway modules. Here, we present single-cell pathway activity factor analysis (scPAFA), an open-source python library designed for large-scale single-cell dataset to rapidly compute PAS and uncover disease-related MOFA-based multicellular pathway modules.…”