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
We perform quality control (QC) on each sample to identify library preparation problems and use a data-driven approach to detect samples that differ substantially from others in the experiment, ensuring more reliable downstream insights. - Normalize Across Samples
To reveal true biological variation, we detect and reduce subtle technical noise that can arise during sample processing. We normalize gene expression signals, filter out low-abundance transcripts, and adjust for known batch effects. - 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, even when individual genes are not statistically significant.
- Build Diagnostic Classifiers
We identify the minimal set of biomarkers that best differentiate sample groups. By evaluating multiple machine learning algorithms and cross-validation strategies, we assess classifier accuracy for potential diagnostic, prognostic, or treatment applications.
Additional Value Out-of-the-Box
RNA-seq 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.
- Alternative Gene Splicing
Since many disorders are influenced by alternatively spliced gene transcripts, our workflow automatically identifies these genes and tests for statistically significant differences in splicing frequency across groups.
- Investigate Drugs for Repurposing
Automated analysis of drug connectivity databases identifies compounds that may reverse the observed phenotype, uncovering new drug repurposing opportunities and highlighting candidates that could counteract disease signatures.
- Microbial Abundance
Sequencing reads that do not map to the reference genome are assessed against bacterial and viral databases. Differential abundance analysis can identify potential contamination and characterize microbial species associated with your samples.
Get in touch to see how our intelligent analysis platform can redefine your multi-omics results.
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