CAMML: Multi-Label Immune Cell-Typing and Stemness Analysis for Single-Cell RNA-sequencing

Inferring the cell types in single-cell RNA-sequencing (scRNA-seq) data is of particular importance for understanding the potential cellular mechanisms and phenotypes occurring in complex tissues, such as the tumor-immune microenvironment (TME). The sparsity and noise of scRNA-seq data, combined with the fact that immune cell types often occur on a continuum, make cell typing of TME scRNA-seq data a significant challenge. Several single-label cell typing methods have been put forth to address the limitations of noise and sparsity, but accounting for the often overlapped spectrum of cell types in the immune TME remains an obstacle. To address this, we developed a new scRNA-seq cell-typing method, Cell-typing using variance Adjusted Mahalanobis distances with Multi-Labeling (CAMML). CAMML leverages cell type-specific weighted gene sets to score every cell in a dataset for every potential cell type. This allows cells to be labelled either by their highest scoring cell type as a single label classification or based on a score cut-off to give multi-label classification. For single-label cell typing, CAMML performance is comparable to existing cell typing methods, SingleR and Garnett. For scenarios where cells may exhibit features of multiple cell types (e.g., undifferentiated cells), the multi-label classification supported by CAMML offers important benefits relative to the current state-of-the-art methods. By integrating data across studies, omics platforms, and species, CAMML serves as a robust and adaptable method for overcoming the challenges of scRNA-seq analysis.