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Updated: Jul 3, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

MarkerMap: nonlinear marker selection for single-cell studies.

Wilson Gregory¹, Nabeel Sarwar², George Kevrekidis¹

¹Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA.

NPJ Systems Biology and Applications

|February 13, 2024

Summary

This summary is machine-generated.

MarkerMap identifies minimal gene sets for cell type identification from single-cell RNA sequencing data. This tool aids in understanding biological variability and reconstructing transcriptomes efficiently.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) enables cell type differentiation analysis.
Identifying key genomic features for cell variability is computationally challenging.

Purpose of the Study:

Introduce MarkerMap, a generative model for selecting informative gene sets.
Enable whole transcriptome reconstruction and improve interpretability in scRNA-seq studies.

Main Methods:

Developed a generative model for marker gene selection.
Implemented supervised and unsupervised selection frameworks.
Benchmarked against existing methods using real scRNA-seq datasets.

Main Results:

MarkerMap efficiently identifies minimal, informative gene sets.
Demonstrated competitive performance against prior methods.
Facilitates both specific cell population identification and gene expression imputation.

Conclusions:

MarkerMap offers a scalable solution for marker gene selection and transcriptome reconstruction.
Enhances the interpretability of single-cell studies through explainable AI.
Provides a valuable, installable resource for the research community.