Seon Min Kim
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ResearchCo-first authorPatent pending

Kidney transplant multi-omics

Brought four to five institutions together and integrated CyTOF, metabolomics and clinical data from kidney transplant patients. Findings are confidential pending a patent application.

28
Patients
~32.3M
Cells profiled across 42 CyTOF markers
When
June 2025 – September 2026
Context
Sarwal and Sirota labs, UCSF
Team
Multi-institution (UCSF, Penn, Madrid, Barcelona)
My role
Co-first author. Data integration, analysis and coordination across institutions
Tools
CyTOF, Metabolomics, Random Forest, Nested cross-validation

Problem

Kidney transplant rejection is hard to predict before transplant. This study asked whether immune and metabolic profiles could help.

My role

I was the only non-faculty researcher who brought four to five institutions together into one collaboration: UCSF, Penn, and groups in Madrid and Barcelona. I integrated CyTOF data (42 markers, about 32.3 million cells) with metabolomics and clinical data for 28 patients, including normalization and batch correction, and built the statistical analysis with Random Forest models, nested cross-validation and multiple-testing correction.

Results

The findings are part of a pending patent application, so I can’t share them here. I’m happy to discuss the methods in an interview.

Publication

Manuscript submitted, co-first author. Poster presented at UCSF Research AI Day 2026.