Mouse-Specific Single cell cytokine activity prediction and Estimation (MouSSE).

Abstract

The accurate cell-level characterization of cytokine activity is important for understanding the signaling processes underpinning a wide range of immune-mediated conditions such as auto-immune disease, cancer and response to infection. We previously proposed the SCAPE (Single cell transcriptomics-level Cytokine Activity Prediction and Estimation) method to address the challenges associated with cytokine activity estimation in human single cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST) data. Here, we propose a new method MouSSE (Mouse-Specific Single cell transcriptomics level cytokine activity prediction and Estimation) for performing cytokine activity estimation in murine scRNA-seq and ST data. MouSSE estimates the cell-level activity of 86 distinct cytokines using a gene set scoring approach. The cytokine-specific gene sets used by MouSSE are constructed using experimental cytokine stimulation data from the Immune Dictionary and cell-level scores are computed using a modification of the Variance-adjusted Mahalanobis (VAM) technique that supports both positive and negative gene weights. MouSSE is validated using data from both the Immune Dictionary via stratified cross-validation and external scRNA-seq and ST datasets against 10 cytokine activity estimation methods. These results demonstrate that MouSSE outperforms comparable methods for cell-level cytokine activity estimation in mouse scRNA-seq and ST data. An example vignette and installation instructions for the MouSSE R package are provided at https://github.com/azkajavaid/MousseR-package.