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Related Experiment Video

Updated: Sep 15, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

PIVOT: an open-source tool for multi-omic spatial data registration.

André Forjaz¹, Valentina Matos Romero^1,2, Ian Reucroft²

¹Department of Chemical & Biomolecular Engineering, Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology

|July 15, 2025

Summary

This summary is machine-generated.

We developed PIVOT, an open-source tool simplifying nonlinear registration for multi-omic atlases. This improves the integration of spatial profiling data across biological scales.

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Area of Science:

Spatial biology
Multi-omics data integration
Bioinformatics

Background:

Spatial profiling technologies generate multi-omic atlases across biological scales.
Integrating these multi-modal datasets typically requires complex, multi-step registration workflows.

Purpose of the Study:

To introduce PIVOT, a user-friendly, open-source interface designed to streamline nonlinear registration.
To enhance the throughput and efficiency of registering multi-omic cohorts.

Main Methods:

Developed PIVOT, a novel software interface for nonlinear registration.
Applied PIVOT to register three distinct multi-omic datasets.
Compared PIVOT's performance against existing registration workflows.

Main Results:

PIVOT successfully registered multi-omic datasets, demonstrating its capability.
The performance of PIVOT was evaluated and compared to established methods.
PIVOT offers a streamlined approach to spatial data integration.

Conclusions:

PIVOT provides an efficient and accessible solution for nonlinear registration in multi-omic studies.
The tool facilitates the integration of spatial profiling data, advancing the creation of comprehensive biological atlases.
PIVOT enhances the throughput for analyzing large-scale multi-omic cohorts.