Classifying diseases using single-cell gene expression

A statistical framework for disease classification with scRNA-Seq data

['FUNDING_R01'] · UNIVERSITY OF CALIFORNIA BERKELEY · NIH-11176961

They are building computer methods that use single-cell RNA data from patients' cells to help identify disease types and guide treatment decisions.

Quick facts

Phase['FUNDING_R01']
Study typeNih_funding
SexAll
SponsorUNIVERSITY OF CALIFORNIA BERKELEY (nih funded)
Locations1 site (BERKELEY, UNITED STATES)
Trial IDNIH-11176961 on ClinicalTrials.gov

What this research studies

This project develops statistical tools to analyze single-cell RNA sequencing (scRNA-Seq) data collected from many patients. The team will create models that link differences among individual cells to overall patient health outcomes. They will test and validate these methods using existing patient datasets and simulations to ensure reliability. The work aims to make scRNA-Seq data more useful for diagnosis and therapy selection in clinical settings.

Who could benefit from this research

Good fit: Patients whose tissue or blood samples are being profiled with single-cell RNA sequencing—such as people in cancer, autoimmune, or other disease cohorts—would be the most directly relevant candidates for related studies.

Not a fit: People who do not have samples collected for scRNA-Seq or whose conditions are not reflected in cellular gene expression are unlikely to see direct benefits from this work in the near term.

Why it matters

Potential benefit: If successful, the tools could help doctors interpret single-cell gene data to improve diagnosis and personalize treatments.

How similar studies have performed: Single-cell sequencing has produced useful biological and research findings, but population-level statistical methods for classifying disease from scRNA-Seq are relatively new and this work is fairly novel.

Where this research is happening

BERKELEY, UNITED STATES

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.

View on NIH RePORTER →

Last reviewed 2026-05-15 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.