Immune Single-Cell Annotation by Molecular Signature-Based Deconvolution with MIXTURE

Abstract

Accurate cell type identification in single-cell RNA sequencing (scRNA-seq) datasets is essential fordissecting the tumor immune microenvironment (TIME) and optimizing immunotherapy strategies.Here, we present an annotation protocol based on MIXTURE, our v-SVR deconvolution algorithm,which addresses the limitations of unsupervised annotation methods. We applied MIXTURE to anannotated melanoma scRNA-seq dataset, demonstrating its effectiveness in enhancing detailed cell typeannotation and composition analysis of unannotated clusters. Our protocol successfully explores thecomposition of clusters, by providing detailed insights into cluster heterogeneity, enabling more accurateand granular identification of distinct cell types within complex single-cell RNA sequencing datasets.