Stratification of Genomic Sequence Complexity and Phenotypes for Precision Medicine

Statistics-first.No reference genome required.Unbiased discovery.

The website is being updated with additional content. Please contact: BD@rosabio.com for more information.

p2single results

Single Cell Demo

Sequence-level models, tested on cells they never saw. Every score is on cells, patients or experiments kept out of training.

Results last updated 2026-10-29 13:29 PST

Open the full demo
10public studies
AUC 0.996best score on cells the model never saw
17published methods beaten in one test
2026-10-29artifact last updated
ROC curve on held-out patientsUnseen patients
AUC 0.978
Immunotherapy · skin cancer

Exhausted vs active killer T cells in patients on immunotherapy

AUC 0.978Patients the model never saw · 2,326 test cellsGSE123813 · Yost et al. 2019
PD-L1 prediction on a new patientTop of 9 methods
p2single0.64
9 published methods0.59
Lung cancer · PD-L1 protein

PD-L1 levels in lung tumour immune cells of a new patient

r = 0.64All 14,903 test cells are from patient 706, never seen in training · trained on patients 626, 630, 695 · AUC on the 10,565 clearly high or low cellsSRP251372 · Leader et al. 2021
More studies8 further results · swipe
AUC per protein-defined group
CD14 monocyte0.999
CD16 monocyte0.997
naive B0.992
memory B0.987
CD8 effector T (CD57+)0.970
CD8 naive T0.965
MAIT0.963
CD4 naive T0.957
Bone marrow · surface protein

Predicting protein-defined cell types in bone marrow from RNA

AUC 0.968GSE128639 · Stuart et al. 2019
Predicted vs measured PD-L1 proteinSeparate experiment
r = 0.74

PD-L1's own RNA alone: 0.38 · high vs low PD-L1 cells: AUC 0.93

CRISPR screen · PD-L1 protein

PD-L1 protein level predicted from RNA, tested on a separate experiment

r = 0.74SRP268462 · Papalexi et al. 2021
AUC per shared cell type in both directionsAcross tissues
artery model → uterus0.88
uterus model → artery0.86
Human Cell Atlas · two tissues

Cell types predicted across tissues and donors

AUC 0.88HCA arterial vasculature (Zhao 2025 · CELLxGENE 8f17ac63-aaba-44b5-9b78-60f121da4c2f) · HCA uterus (Ulrich 2024 · GSE260658)
Macro F1 on Xin: p2single and 17 published methodsTop of 17 methods
p2single1.000
scGenoByte0.999
scRGCL0.994
CIForm0.991
scGraph0.985
scGPT0.980
ACTINN0.966
SingleR0.952
Human pancreas · cell types

Pancreas cell types: at or above every published method

45 of 46Xin 2016 · GSE81608
Transfer AUC by species pairAcross species
human → mouse (4 tests)0.932
macaque → human (56)0.853
between 2 macaque species (20)0.950
human → other humans (56)0.973
Brain · across species

Brain cell types predicted across species

AUC 0.932Allen Brain Cell Atlas HMBA-10xMultiome-BG (release 20250630) · human, macaque, mouse
Cosine similarity, Norman screenTop of 4 methods
p2single0.890
latent additive0.790
CPA0.760
linear0.600
GEARS0.440
CRISPR screens · gene response

How cells respond when 2 genes are changed at once

0.890GSE133344 · Norman 2019 · PerturBench
AUC per egg-development stage
stem cells, progenitors0.997
early meiosis0.997
meiosis0.987
late meiosis0.994
early egg stage 10.997
early egg stage 20.998
early egg stage 30.999
Zebrafish ovary · germ-cell stages

Stages of egg development in the young zebrafish ovary

AUC 0.996GSE191137 · Liu et al. 2022
Share of cells with KRAS G12V
Patient 6: tumour epithelial cells17 of 18
Patient 6: immune cells0 of 22
Other 9 patients: all cells0 of 515
Variant calling · single cell

Cancer mutations found in individual cells

NRAS G12DSRP268462 · Papalexi et al. 2021 · GSE316782 · Xi et al. 2026

Benefits of Rosa Bio Technology

  • ROSA's "statistics first" Semore platform allows detection of genomic complexity missed by other methods
  • Allows faster, cheaper, smarter genomics analysis
  • Readily scalable
  • Agnostic to nucleic acid type: DNA (WGS and targeted), RNA, short and long read, exome, non-coding regions, ctDNA, bisulfite, etc.
  • Detects structure-based changes, not based on gene panels
  • Can be used with tissue sample or liquid biopsy (plasma cfDNA)
  • Software = AI ready
  • Diverse applications: Dx, Tx, other

Panoptic. Faster. Cheaper.

Speed

Faster processing, lower overhead.

Accuracy

Assess all sequence variation in a sample with no bias.

Cost

High-efficiency analysis tools.

A Statistics-First Approach.

A new paradigm for analysis of nucleic acid sequence information. Unlike traditional methods, we require no reference genome for sequence alignment.

Our approach enables truly unbiased discovery, identifying patterns and variations that conventional tools simply cannot detect.

Why Statistics First

Our statistics-first approach analyzes raw data directly, eliminating reference genome bias and enabling detection of all genomic complexity.

Precision Medicine Applications

Patient stratification using sequence variation that predicts treatment response and disease progression.

Leadership

JS

Julia Salzman

Founder

Associate Professor in the Department of Biomedical Data Science, and by courtesy in Biology and Statistics at Stanford University. A.B. in Mathematics from Princeton University magna cum laude and Ph.D. from Stanford University in Statistics. Developed statistical algorithms that discovered circular RNA and statistics-first genomic algorithms. Research funded by NIH, NSF, CZI. Recognized with Alfred P. Sloan Fellowship, McCormick-Gabilan Fellowship, and NSF CAREER Award.

MR

Michael Rabson

Co-Founder & CEO

More than 25 years of experience in life sciences. Former partner at Wilson Sonsini Goodrich & Rosati for 15 years. Served as General Counsel of Maxygen (MAXY) and Cytokinetics (CYTK). Former patent examiner at USPTO focused on biotechnology. Member of the patent bar. Author of scientific publications in Science, Nature, PNAS. Ph.D. from Yale University and J.D. from Yale Law School.