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Atlas-Level Single-Cell and Spatial Transcriptomics Data Integration via PRIME.

Xinchao Wu¹, Xuefei Wang², Jieqiong Wang³

¹Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, Omaha, NE.

Biorxiv : the Preprint Server for Biology

|June 4, 2026

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This summary is machine-generated.

PRIME is a new computational framework for integrating large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. It effectively reconciles batch effects while preserving biological insights for cell atlases.

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Atlas-scale cellular cartography using scRNA-seq and ST is crucial for understanding cell identity and disease mechanisms.
Integrating heterogeneous data from multiple sources, donors, and technologies presents significant computational challenges, especially with imbalanced cell-type compositions.
Existing integration methods often struggle to balance batch effect correction with the preservation of subtle biological variations and spatial information.

Purpose of the Study:

To introduce PRIME (Projection-based Robust Integration via Manifold Embedding), an ensemble framework for robust atlas-level integration of scRNA-seq and ST data.
To develop a method that can jointly reconcile complex batch effects across numerous datasets while preserving biological structure and accommodating spatial coordinates.
To provide a computationally tractable solution for integrating millions of cells.

Keywords:

Batch effect correction Single-cell RNA-seq Single-cell perturbation Spatial Transcriptomics Trajectory inference

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Main Methods:

PRIME employs an ensemble approach combining random-projection-based consensus anchoring and graph-Laplacian correction.
Consensus voting across random projections identifies reliable cell-pair anchors, reducing projection-specific distortions.
For ST data, PRIME integrates expression-based anchors with spatial coordinates using a graph-Laplacian objective for simultaneous alignment and spatial coherence.

Main Results:

PRIME consistently outperforms state-of-the-art methods in batch correction and biological conservation for both scRNA-seq and ST integration.
The framework successfully preserves developmental trajectories in human hematopoiesis and cortical laminar architecture in spatial transcriptomics data.
PRIME accurately recovers drug-target relationships in a large perturbation atlas (>1 million cells) while mitigating batch effects.

Conclusions:

PRIME offers a versatile and scalable solution for integrating large-scale scRNA-seq and ST datasets.
The framework demonstrates superior performance in preserving biological accuracy and spatial information across diverse applications.
PRIME advances the field of single-cell and spatial data integration, enabling more comprehensive cellular atlases and deeper biological insights.