Daniel Dimitrov

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

RIDDEN predicts receptor activity from receptor-regulated gene expression, outperforming methods that use ligand-receptor co-expression, and identifies mechanistic biomarkers in cancer patients.

MetalinksDB provides a comprehensive database of metabolite-protein interactions, enabling context-specific analysis of metabolite-mediated cell-cell communication.

This paper integrates LIANA and Tensor-cell2cell to analyze cell-cell communication across multiple samples, enabling robust and flexible identification of communication programs.

The paper introduces a framework for analyzing cross-condition single-cell atlases to identify multicellular disease programs, validated across multiple datasets.

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics data.

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

decoupleR evaluates methods for inferring biological activities from omics data, finding simple linear models and consensus scores perform best in perturbation experiments.

BingleSeq provides a user-friendly R package for analyzing bulk and single-cell RNA-seq data without requiring programming expertise.