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Related Experiment Video

Updated: Aug 10, 2025

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

Published on: March 29, 2019

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.

Yusuf Baran¹, Berat Doğan¹

¹Department of Biomedical Engineering, Inonu University, Malatya, Turkey.

Computers in Biology and Medicine

|February 12, 2023

Summary

scMAGS is a new method for selecting marker genes from single-cell RNA sequencing data. This approach enhances spatial transcriptomics by accurately identifying cell types, even in large datasets.

Keywords:

Marker gene selection Spatial transcriptomics scRNA-seq

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Last Updated: Aug 10, 2025

In vivo Application of the REMOTE-control System for the Manipulation of Endogenous Gene Expression

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Published on: March 29, 2019

In Vitro Selection of Engineered Transcriptional Repressors for Targeted Epigenetic Silencing

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Published on: May 5, 2023

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers insights into gene expression but loses spatial context.
Spatial transcriptomics preserves spatial information but requires accurate cell type identification using marker genes.

Purpose of the Study:

To introduce scMAGS (single-cell MArker Gene Selection), a novel computational method for selecting marker genes from scRNA-seq data.
To improve cell type identification in spatial transcriptomics studies.

Main Methods:

scMAGS employs a filtering step to identify candidate genes.
Marker gene selection utilizes cluster validity indices like Silhouette or Calinski-Harabasz for large datasets.

Main Results:

scMAGS demonstrates scalability, speed, and accuracy compared to existing methods.
Efficiently identifies marker genes for large datasets with millions of cells, requiring less memory.

Conclusions:

scMAGS is a robust and efficient tool for marker gene selection in spatial transcriptomics.
The method facilitates accurate cell type identification, advancing spatial biology research.