Identifying Similar Astrocyte Populations in Motor Cortex and Spinal Cord Across Independent Single Cell Studies Without Data Integration

Abstract

Supervised and unsupervised methods have emerged to address the complexity of single cell data analysis in the context of large pools of independent studies. Here, we introduce ClusterFoldSimilarity (CFS), a novel statistical method that quantifies the similarity between cell groups from any number of independent datasets, without the need for data correction or integration. It avoids the introduction of artifacts and loss of information by bypassing these processes, offering a simple, efficient, and scalable solution. It can be used to match groups of cells that exhibit conserved phenotypes across datasets, including different tissues and species, and in a multimodal scenario, including single-cell RNA-Seq, ATAC-Seq, single-cell proteomics, or, more broadly, data exhibiting differential abundance effects among groups of cells. Additionally, CFS performs feature selection, obtaining cross-dataset markers of the similar phenotypes observed. To showcase the effectiveness of our methodology we generated single-nuclei RNA-Seq data from the motor cortex and spinal cord of adult mice. By using CFS, we identified three distinct sub-populations of astrocytes conserved on both tissues. CFS includes various visualization methods for the interpretation of the similarity scores and similar cell populations.

Keywords: Single cell methods, Multi-modal single cell analysis, Astrocyte biology, cluster similarity, motor neuron diseases, cell marker discovery