Standard analysis
The standard bioinformatic analysis includes sc/snRNA sequencing data consolidation, counting and quantification, quality control, normalization, batch effect correction, cell clustering, cell type annotation, differential gene expression in major cell types, and data visualization.
Customized bioinformatic analysis
For specific biological questions, our services include additional cell sub-clustering, differential gene expression in distinct cell types, Gene Ontology analysis, single-cell trajectory inference, co-expression network analysis, cell-cell interaction analysis, machine, and deep learning-based analysis and predictions.
AI-driven omics analysis
Our team leverages the power of explainable machine learning (ML) models to integrate extensive biological datasets. Among many applications, our ML models can:
- Uncover molecular mechanisms linked to drug efficacy;
- Predict treatment toxicities;
- Predict drug efficacy and combination synergies;
- Predict repurposing of existing drugs for new therapeutic uses;
- Predict patient-specific responses to therapies;
- Identify disease-related signatures;
- Identify drug development targets and diagnostic biomarkers.