V. Savova

DiSiR: a software framework to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

DiSiR identifies ligand-receptor interactions at the subunit level from scRNA-seq data, outperforming existing methods in inferring disease-related pathways.

Cell type classification and discovery across diseases, technologies and tissues reveals conserved gene signatures and enables standardized single-cell readouts

The paper introduces Signac, a machine learning method for classifying single-cell RNA-sequencing data across diseases and tissues, and identifies drug targets in rheumatoid arthritis.

An optimized workflow for single-cell transcriptomics and repertoire profiling of purified lymphocytes from clinical samples.

The paper optimizes workflows for single-cell transcriptomics and repertoire profiling of purified lymphocytes from clinical samples, identifying best practices for viability, yield, and data quality.