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Related Experiment Video

Updated: Jun 24, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes.

Yinglu Jia^1,2, Pengchong Ma¹, Qiuming Yao^3,4,5

¹School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA.

Scientific Reports

|June 7, 2024

Summary

This summary is machine-generated.

Identifying cell-type specific marker genes from single-cell RNA sequencing (scRNA-seq) data is streamlined by cellMarkerPipe. This novel platform automates gene identification and evaluation, enhancing efficiency for cellular biology and medical research.

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Last Updated: Jun 24, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Automated identification and benchmarking of cell-type specific marker genes from single-cell RNA sequencing (scRNA-seq) data are crucial for biological and medical research.
Current methods for assessing marker genes across cell clusters are often time-consuming and lack systematic strategies, hindering efficient and fair evaluation.

Purpose of the Study:

To develop a unified computational platform, cellMarkerPipe, for automated cell-type specific marker gene identification from scRNA-seq data.
To create a comprehensive evaluation schema for benchmarking different marker gene identification tools.
To enhance the efficiency and systematic evaluation of marker gene identification in scRNA-seq data analysis.

Main Methods:

Developed cellMarkerPipe, an open-source computational pipeline integrating multiple established and state-of-the-art tools (Seurat, COSG, SC3, SCMarker, COMET, scGeneFit).
Implemented an adaptive wrapping strategy to incorporate various analysis tools.
Designed a comprehensive evaluation schema for benchmarking tool performance.
Tested the pipeline across diverse scRNA-seq datasets, including real-world medical data.

Main Results:

SCMarker demonstrated reliable performance for single marker gene selection.
COSG exhibited commendable speed with comparable efficacy to other tools.
The cellMarkerPipe platform successfully streamlined marker gene identification and evaluation across various datasets.
Demonstrated the utility of the platform in real-world medical applications.

Conclusions:

The cellMarkerPipe pipeline provides a general, open-source solution for automated cell-type specific marker gene identification and evaluation from scRNA-seq data.
The platform significantly enhances efficiency and ensures fair benchmarking of analysis tools.
This advancement offers broad applications in cellular biology and medical research, facilitating more robust data interpretation.