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

Abstract

Single-cell atlases across conditions are essential in the characterization of human disease. In these complex experimental designs, patient samples are profiled across distinct cell-types and clinical conditions to describe disease processes at the cellular level. However, most of the current analysis tools are limited to pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes and the effects of other biological and technical factors in the variation of gene expression. Here we propose a computational framework for an unsupervised analysis of samples from cross-condition single-cell atlases and for the identification of multicellular programs associated with disease. Our strategy, that repurposes multi-omics factor analysis, incorporates the variation of patient samples across cell-types and enables the joint analysis of multiple patient cohorts, facilitating integration of atlases. We applied our analysis to a collection of acute and chronic human heart failure single-cell datasets and described multicellular processes of cardiac remodeling 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 atlas and allows for the integration of the measurements of patient cohorts across distinct data modalities, facilitating the generation of comprehensive tissue-centric understanding of disease.

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

“…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.…”

“…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.…”

Single nuclei RNAseq stratifies multiple sclerosis patients into distinct white matter glial responses

“…This demonstration that patients with long-standing MS differ markedly in the transcriptional signatures of their glia, whatever their lesion classification, but which fall into apparent subgroups, is important in the context of the variable responses to experimental neuroprotective therapies for example targeting remyelination. To explore these apparent subgroups more, and to understand the underlying cellular and molecular mechanisms, we adapted MOFA+, a computational method originally developed for identifying low-dimensional representations of variation across multiple data modalities 29 to identify similar donor-associated transcriptional patterns across multiple cell types (modalities) simultaneously 29,30 . For each cell type, we selected genes with evidence of a MS effect and/or a donor effect (Extended Data Fig.…”

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

“…Moreover, most tools do not account for the relationships of coordinated CCC events (CCC programs) across different contexts 4 , either disregarding context altogether by analyzing samples individually or being limited to pairwise comparisons. Thus, as the ability to generate large single-cell and spatial transcriptomics datasets and the interest in studying CCC programs continues to increase [5][6][7] , the need to robustly decipher CCC is becoming essential.…”