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Updated: Aug 11, 2025

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

scTOP: physics-inspired order parameters for cellular identification and visualization.

Maria Yampolskaya¹, Michael Herriges^2,3, Laertis Ikonomou^4,5

¹Department of Physics, Boston University, Boston, MA 02215, USA.

Biorxiv : the Preprint Server for Biology

|February 7, 2023

Summary

This summary is machine-generated.

We developed single-cell Type Order Parameters (scTOP), a physics-inspired method for cell fate classification using single-cell RNA sequencing (scRNA-seq) data. scTOP accurately analyzes cell development and engineered cells without complex computational steps.

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Last Updated: Aug 11, 2025

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

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Published on: September 7, 2018

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Area of Science:

Computational Biology
Systems Biology
Developmental Biology

Background:

Single-cell RNA sequencing (scRNA-seq) generates vast amounts of data, necessitating advanced computational tools for cell fate determination and atlas integration.
Current methods often rely on feature selection, statistical fitting, or dimensionality reduction, which can limit interpretability and accuracy.

Conclusions:

Physics-inspired order parameters offer a powerful and interpretable approach for analyzing complex single-cell data.
scTOP provides a computationally efficient and accurate tool for understanding cellular development and characterizing engineered cells.
The scTOP framework advances the field of single-cell data analysis, enabling deeper insights into cell fate determination and biological processes.