A collaborative analysis platform for researchers and bioinformaticians to investigate multimodal biological data and accelerate discoveries.
Discover and save signatures from many aspects of omics (expression, mutation, methylation, and others). Use these signals to reveal patterns across public and proprietary data.
Oncology & Immuno-Oncology
Use public oncology datasets to assess omics across cancers and immune subtype. Compare single-cell to bulk tissue results to mine data at tissue and cellular levels of resolution.
Use cohort comparison tools to reveal differential markers in tumor vs. normal, responder vs. non responder. Make differential gene expression lists with a single click.
Integrate data from multiplex diagnostics into a single report. Review and analyze data from your testing population for correlations or trends.
Build quantitative evidence that accurately identifies promising early research and validate discoveries so that you can confidently move into clinical care.
Zoom in and out to get a 360 degree view of an individual’s health information, from genomics details to complex health conditions, to prescribe personalized treatments.
Below are examples of analysis apps used in life sciences. New apps can be built and deployed in under an hour to provide analysis capabilities unique to your data.
Gene Expression Signatures
Define a gene signature as the single-sample score for a set of selected genes. This signature can then be run over all disease and tumor types to identify significant differences.
Define a mutation signature as a cohort of patients having specific mutations. This profile can then be run over all disease and tumor types to identify significant differences.
Single-Cell Gene Expression
Perform UMAP embedding for selected cells and selected genes, followed by clustering with k-means or DBSCAN.
Cell Type Gene Expression Comparison
Find significantly differentially expressed genes for the specified cell type(s), compared to the other cell types in the background cohort.
Perform cox survival analysis on a patient cohort that you define via disease type and mutation status.
Identify features correlated with limited survival times. These features can be the basis of studies for what defines poor prognosis in a particular cancer.
Gene Signature Heatmap Using R
Create a heatmap of selected gene expression using an integrated R script. This analysis app is an example of how you can incorporate scripts and algorithms into a node.
Elastic Net Cross Validation
Elastic net cross validation via a Python implementation of an ML algorithm. Reparameterize this predictive model and assess the importance of patients in your test set or input features.
Want to see more examples of analysis apps in life sciences? Get in touch!
We have hundreds of data nodes and analysis apps at your disposal. Let us know what types of questions you have and what areas you want to focus on and we will give you a personalized demo.
“There is no piece of software that does what you do. If there was we would be using it.”
– Data Science Department Director
@Top 5 Global Pharmaceuticals Company
Specialized Analytics for Everyone
Staff at all levels can confidently make data driven decisions using guided, investigation-driven analysis apps tailored to their area of expertise.
Information and Innovation Officers
Consolidate tools, maximize ROI and keep your organization on the cutting edge by providing your scientists a powerful tool that allows them to be 1000x times more efficient.
Researchers and Domain Experts
Speed up drug discovery with the ability to run reproducible and configurable analysis on the fly. Contextualize your data better with gene, variant and pathway annotation and compare your findings to public datasets. Utilize public data to validate your results.
Data Scientists and Bioinformaticians
Keep up with research questions by building point-and-click analysis apps that empower your researchers to run analyses on their own and enable them to reproduce and share their analyses with the team.