PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data
- 1Stanford University
Abstract
Single-cell and spatial transcriptomic studies often lack sufficient sample size to compute robust statistical associations between a sample-level phenotype and cell types or spatial locations. In contrast, lower resolution methods such as bulk gene expression profiling have been applied at scale in large, annotated datasets, providing reliable signatures for phenotype associations. We introduce PhenoMapR, a semi-supervised method designed to integrate the phenotypic rigor of large-scale bulk expression studies with the cellular and spatial granularity of single-cell and spatial transcriptomics. PhenoMapR achieves this by deriving and mapping bulk gene expression signatures onto cells and spatial locations in a computationally efficient and scalable manner. The framework is broadly applicable across biological contexts, supporting the mapping of binary, continuous, and survival phenotypes derived from bulk expression studies across transcriptomic data modalities. This enables the identification of biologically-relevant cellular populations and spatial niches for experimental validation and therapeutic intervention.
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BibTeX
@misc{benard2026phenomapr,
title = {{PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data}},
author = {B. A. Benard and C. K. Lalgudi and A. Azizi and A. J. Gentles},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.749933},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.749933v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data
AU - B. A. Benard
AU - C. K. Lalgudi
AU - A. Azizi
AU - A. J. Gentles
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.749933
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.749933v1
AB - Single-cell and spatial transcriptomic studies often lack sufficient sample size to compute robust statistical associations between a sample-level phenotype and cell types or spatial locations. In contrast, lower resolution methods such as bulk gene expression profiling have been applied at scale in large, annotated datasets, providing reliable signatures for phenotype associations. We introduce PhenoMapR, a semi-supervised method designed to integrate the phenotypic rigor of large-scale bulk expression studies with the cellular and spatial granularity of single-cell and spatial transcriptomics. PhenoMapR achieves this by deriving and mapping bulk gene expression signatures onto cells and spatial locations in a computationally efficient and scalable manner. The framework is broadly applicable across biological contexts, supporting the mapping of binary, continuous, and survival phenotypes derived from bulk expression studies across transcriptomic data modalities. This enables the identification of biologically-relevant cellular populations and spatial niches for experimental validation and therapeutic intervention.
ER -