From Data to Value in Hours
Our AI-powered analysis automates each step, rapidly delivering results as an interactive web application that can be explored and shared across all stakeholders.
- Identify Relevant Clinical Variables
Clinical information (e.g., age, sex, BMI, drug use) is used to identify the most relevant molecular changes. We compare AI-based predictions with supplied metadata to detect and correct labeling errors and select the minimal set of relevant clinical variables to preserve statistical power in subsequent steps. - Assess Quality
Robust quality control is crucial for reliable insights. Our pipeline automatically filters technical artifacts by assessing MultiQC reports, detecting doublets and empty droplets, and removing ambient, mitochondrial, and ribosomal RNA contamination. - Normalize Across Samples
Unexpected differences between cells and samples may arise due to subtle technical variability in sample processing. Our process eliminates this noise to reveal the underlying biology by standardizing gene expression across all cells, and adjusting for known batch effects. This is followed by a visual validation of the normalization using tSNE and UMAP plots. - Cell-Type Identification
Accurate cell population annotation is key for meaningful biological interpretation, cross-study comparisons, and robust downstream analysis. We use both reference-based and data-driven methods for the most accurate results. - Find Differentially Expressed Genes
Depending on your experimental design and research question, we select the most appropriate, statistically rigorous approach. We account for technical and biological variability, including cellular heterogeneity, and automatically fit an appropriate linear regression model to each gene. - Pinpoint Affected Pathways
We perform gene set enrichment analysis (GSEA) across all major pathway databases, clustering similar pathways to explore how groups of genes act together in different cell populations, even when individual genes are not statistically significant.
Additional Value Out-of-the-Box
Single cell and spatial transcriptomics data contains a wealth of valuable information that often goes unexplored. To ensure you do not miss key insights, these analyses are included as part of our standard workflow.
- Communication of Cell-Niches
Map the communication between cell niches. We pinpoint interactions between distinct cell populations by identifying the signaling pathways and ligand-receptor pairs that mediate intercellular communication. This analysis provides critical insights into cellular crosstalk and coordinated biological processes. - Spatially Variable Genes
Identify spatially variable genes by detecting differential expression prior to cell type annotation. This approach provides a comprehensive view of the average transcriptional response across the entire system. - Diagnostic Classifiers
Develop powerful diagnostic classifiers. We identify the minimal set of biomarkers that best differentiate sample groups, either globally or within specific cell types. By evaluating multiple machine learning algorithms and cross-validation strategies, we assess classifier accuracy for potential diagnostic, prognostic, or treatment applications.
Get in touch to see how our intelligent analysis platform can redefine your multi-omics results.
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