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Updated: Jan 9, 2026
Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
Published on: March 25, 2020
Analysis of clinical, single cell, and spatial data from the Human Tumor Atlas Network (HTAN) with massively
David Gibbs1, Dar'ya Pozhidayeva1, Yamina Katariya1
1Institute for Systems Biology, Seattle, WA, USA.
The Human Tumor Atlas Network (HTAN) created a cloud platform to analyze complex cancer data. This infrastructure simplifies integrating multimodal datasets and analyzing spatial biology, accelerating cancer research discoveries.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Cancer research generates large, complex multimodal datasets (e.g., single-cell transcriptomics, proteomics, imaging).
- The Human Tumor Atlas Network (HTAN) produces these datasets, but data volume and heterogeneity pose integration and analysis challenges.
- Researchers need scalable, accessible tools to explore and analyze tumor ecosystem data.
Purpose of the Study:
- To develop a cloud-based infrastructure for integrating and analyzing large-scale, multimodal cancer data from HTAN.
- To introduce innovations for simplified cohort construction, cross-assay integration, and spatial biology analysis.
- To lower technical barriers for researchers accessing and analyzing complex cancer datasets.
Main Methods:
- Developed a cloud infrastructure hosted by the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC).
- Transformed clinical and assay metadata into aggregate Google BigQuery tables.
- Implemented a provenance-based HTAN ID table for cohort construction and cross-assay integration.
- Adapted BigQuery's geospatial functions for spatial biology analysis (neighborhood and correlation).
Main Results:
- Successfully created a cloud infrastructure enabling scalable analysis of HTAN multimodal data.
- Demonstrated simplified cohort construction and cross-assay integration using the HTAN ID table.
- Enabled neighborhood and correlation analysis of tumor microenvironments using adapted BigQuery geospatial functions.
- Provided R and Python notebooks showcasing use cases like cohort identification and multimodal data integration.
Conclusions:
- The HTAN cloud infrastructure effectively addresses challenges in analyzing large, heterogeneous cancer datasets.
- Innovations in data integration and spatial analysis tools accelerate cancer discovery.
- Cloud-based platforms offer a cost-effective and intuitive entry point for cancer researchers, fostering collaboration and advancing the field.
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