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Related Experiment Video

Updated: Aug 30, 2025

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

Multiscale Methods for Signal Selection in Single-Cell Data.

Renee S Hoekzema^1,2, Lewis Marsh^1,3, Otto Sumray^1,3

¹Mathematical Institute, University of Oxford, Oxford OX1 2JD, UK.

Entropy (Basel, Switzerland)

|August 26, 2022

Summary

This summary is machine-generated.

We introduce three novel unsupervised methods for analyzing single-cell transcriptomics data. These topological approaches identify genes with continuous and discrete expression patterns, revealing new biological insights.

Keywords:

feature selection multiscale data analysis persistent Laplacian single cell transcriptomics topological signal processing

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Area of Science:

Computational Biology
Genomics
Data Science

Background:

Single-cell transcriptomics analysis commonly uses clustering and differential gene expression (DGE).
These methods primarily identify discrete cell types and markers.
Continuous biological variation within and between cell types may be missed.

Purpose of the Study:

To develop unsupervised feature selection methods for single-cell transcriptomics.
To analyze both discrete and continuous transcriptional patterns simultaneously.
To identify genes with coherent expression patterns across multiple scales.

Main Methods:

Eigenscores (eigi): Spectral decomposition of Laplacian graph for low-frequency patterning.
Multiscale Laplacian score (MLS): Identifies relevant scales and coherently expressed genes.
Persistent Rayleigh quotient (PRQ): Separates genes involved in bifurcation processes (e.g., pseudotime).

Main Results:

Applied methods to published single-cell transcriptomics datasets.
Validated previously identified marker genes.
Discovered additional biologically meaningful genes with coherent expression patterns.
Provided multidimensional gene rankings and visualizations of gene relationships.

Conclusions:

The proposed topological methods offer a powerful alternative to traditional DGE analysis.
These techniques capture complex gene expression patterns, including continuous variation.
The methods enhance the understanding of cellular heterogeneity and dynamics.