SignallingIntelligence reconstructs signal transduction and phosphoprotein networks from single-cell protein and phosphoprotein data, then uses them to quantify drug-treatment effects and prioritise new therapeutic targets. The platform combines peer-reviewed mechanistic tools with AI-driven models, keeping the biology interpretable at every step.
ESGI resolves multi-pattern combinatorial barcodes for Phospho-seq and SIGNAL-seq single-cell sequencing data with high read-recovery accuracy.
Our normalization method performs cell-size normalisation for single-cell proteomics, separating biological concentration from technical abundance and library-size effects.
LoCo finds co-varying protein and phosphoprotein pairs within cell-state neighbourhoods, resolving signalling relationships invisible to global correlation.
Correlation programs are mapped onto SIGNOR and OmniPath to reconstruct active signal transduction and phosphoprotein networks, then compared across cell states.
Because drug response and resistance are driven by signalling network rewiring at the protein level — often invisible to transcriptomics — single-cell phosphoproteomics lets us see how a treatment changes signalling flow and pinpoint the nodes that drive each cell's behaviour, revealing new therapeutic targets.