Identifying similar populations across independent single cell studies without data integration

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 present ClusterFoldSimilarity (CFS), a novel statistical method design to quantify the similarity between cell groups across any number of independent datasets, without the need for data correction or integration. By bypassing these processes, CFS avoids the introduction of artifacts and loss of information, offering a simple, efficient, and scalable solution. This method 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, providing an inherent interpretability of relationships between cell populations. 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.