A single platform for research data harmonization and analysis. Unparalleled collaboration between your workforce of scientists and multimodal data sources.
Data Driven Transformation In Drug Discovery Process
Bring new treatments to market faster. Significantly improve success rates by closing the data question-to-answer loop, from basic discovery to clinical trials to real world evidence.
Basic Research
Tag.bio used to identify novel targets, pathways, biomarkers, and signatures.
Preclinical
Tag.bio used to evaluate potential success of a treatment, using data from cell lines and model organisms.
Clinical Trials
Tag.bio used to stratify heterogeneous patient populations.
Tag.bio used to develop diagnostics for stratification, toxicity, and response to treatment.
Regulatory Review
Tag.bio used to produce quality controlled reports for reviews.
Real World Evidence
Tag.bio used to connect clinical endpoint biomarkers with real world data.
See Solutions For Pharma & Biotech
See Solutions For Translational Oncology
Solve complex data challenges in clinical research
Manage, harmonize, and analyze multimodal biological data from a single platform. Perform statistical analysis and data science by point-and-click and via pluggable R/Python scripts.
Single Cell Analysis
Make analyzable data products that discover biomarkers and explore heterogeneity in single-cell multiomics. Use apps to study genomics, transcriptomics, proteomics and metabolomics across individual cells and populations of cells.
Gene Expression
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.
Biomarker Discovery
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.
Clinical Data Management
Clinical trials frequently involve similar data workflows, however the data is typically siloed into single trial sets. With the Tag.bio analysis platform, you can turn all clinical trial data sources into harmonized, instantly analyzable data products.
Use case examples:
- Analyze and manage data products for a particular therapeutic as it proceeds or fails through different phases of clinical trials
- Group different data products into categories for useful data discovery and cataloging
- Assign and manage user, group, or departmental access to different data products
Machine Learning & AI
Use a single platform to access cleaned, harmonized data products for your algorithms.
- AWS, Azure & Google Cloud
- R, Python SDK, JupyterHub/JupyterLab Notebook & plugins
- Integrated AutoML, SageMaker, Azure ML and Google AI services
- Supports TensorFlow, PyTorch, mxnet, Keras, GLUON, SciML & DeepGraphLibrary
Single Cell Gene Expression
In this demo, we’re going to show you how you can use a no-code analysis app to look for genes that are differentially expressed in cancer cells.
Gene Expression Signatures
In this demo, we’re going to show you how to view the level of expression of a gene signature across cancer types using a no-code analysis app.
Explore Resources For Life Sciences
Data Harmonization for Translational Research
A practical guide to help you resolve clinical trial data challenges using the data mesh approach.
Promote Transparency In The Data Analysis Process
Iterative Analysis Allows Faster Discoveries
Get A Demo
See how Tag.bio works within your clinical workflow.
Redefine How Data Science Advances Clinical Research
Use no-code analysis apps, embedded in your data products, to perform a wide range of analysis — from a simple descriptive analysis to a complex machine learning algorithm.
Statistics currently available:
- Hypergeometric test for categorical data and sets
- Student’s T and Mann-Whitney U tests for numeric distributions
- Univariate and Multivariate Linear Regression for numeric relationships
- Paired analysis for repeated (longitudinal) measures
- Matched analysis for control of confounding variables
- Cox regression for survival analysis
- K Means and DBSCAN clustering for segmentation
- PCA, t-SNE and UMAP for projection/embedding
- Fast event sequence queries
- Pathway (systems) analysis via Hypergeometric test and GSEA
- Gene signature analysis via ssGSEA
- Chi-square test for categorical data
- Logistic Regression and Random Forest (also many other options) for prediction/classification
- Pearson/Spearman correlation for numeric distributions
Examples of data types:
- DNA-Seq (VCF, MAF)
- RNA-Seq (bulk and single-cell, spatial transcriptomics)
- Proteomics
- Flow cytometry
- Compound screening
- Immune repertoire
- Clinical trials (outcomes and biomarkers)
- Longitudinal studies
- Annotation, ontology, pathways
- Machine behavior and maintenance
- Drug response & pKa studies
- Biomanufacturing yields
- Knockdown studies (RNAi, CRISPR)
- Meta genomics
- Gene expression
- DNA methylation
- Somatic mutations
- Germline variants
- High content screening
Examples of data sources:
- cBioPortal
- TCGA
- dbGaP
- GEO & ArrayExpress
- Clinical trials
- UK Biobank
Data Product Examples
TCGA Pan-Cancer Atlas And The Immune Landscape Of Cancer
This data product is a combination of data from two sources: TCGA Pan-Cancer Atlas and the Immune Landscape of Cancer. It has 10,967 samples from 33 cancer types.
Single Cell RNA-Seq
This data product contains ~6000 cells from 18 patients with head and neck cancer (HNSC). The dataset was published by Puram et al. in Cell in 2017.
Analysis App Examples
Clustering
Perform UMAP embedding for selected entities, followed by clustering with k-means.
Python Integration
Turn Python models into analysis apps.
Analysis Result Examples
R Integration
Python Integration
UMAP & Clustering
Cox Survival
Pathway Analysis
Cohort Comparison
“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
One platform. Multiple uses.
The flexibility and adaptability of Tag.bio’s platform can help you significantly advance the field of biomedical research.
Translational Oncology
Convert static clinical trial data files into harmonized, analyzable data products. Tag.bio offers a secure, scalable, and flexible approach to help researchers, clinicians, and data scientists identify and implement new discoveries through iterative analysis of data.
Oncology & Immuno-Oncology
Use public oncology datasets to assess multi-omics across cancers and immune subtypes. Compare single-cell to bulk tissue results to mine data at tissue and cellular levels of resolution.
Precision Medicine
Reduce the cost of delivering precision medicine. Tag.bio facilitates collaboration between healthcare providers and pharma organizations. The flexibility of Tag.bio platform can harmonize a variety of data sources and formats, from EHR to multi-omics.
Harmonized Therapeutic Areas
Since immunology connects to many therapeutic areas, such as immuno-oncology, inflammation, and autoimmunity, you can use a single analysis platform to instantly pivot between data products from each of these therapeutic areas.
Data Security in Clinical Research Environment
Tag.bio is hosted entirely within your secure network and/or your secure cloud. Source data storage and access is tightly controlled within your network, and the platform is compliant with standards such as GxP, GDPR and HIPAA.
From a 30-minute demo to an inquiry about our 4-week pilot project, we are here to answer all of your questions!
