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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Highly Regional Genes: graph-based gene selection for single-cell RNA-seq data.

Yanhong Wu¹, Qifan Hu¹, Shicheng Wang¹

¹MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|February 10, 2022

Summary

This summary is machine-generated.

Highly Regional Genes (HRG) is a novel method for selecting informative genes from single-cell RNA-seq data. HRG outperforms existing methods in accuracy and robustness, enhancing downstream analyses.

Keywords:

Feature selection Graphical models Regional patterns Single-cell RNA-sequencing Spatially resolved transcriptomic data

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA-sequencing (scRNA-seq) data is high-dimensional and noisy, necessitating effective gene selection.
Traditional variance-based methods may not capture biologically relevant gene expression patterns.
Identifying informative genes is crucial for accurate interpretation of scRNA-seq data.

Purpose of the Study:

To introduce a new gene selection method, Highly Regional Genes (HRG), for scRNA-seq data analysis.
To develop a method that identifies genes with regional expression patterns within cell-cell similarity networks.
To evaluate the performance of HRG compared to existing unsupervised gene selection techniques.

Main Methods:

HRG mimics human marker selection by identifying genes with regional expression patterns in a cell-cell similarity network.
A scoring function is mathematically optimized to find the most informative gene expression patterns.
The method was validated against several unsupervised gene selection approaches.

Main Results:

HRG demonstrates high accuracy and robustness in gene selection compared to other methods.
The application of HRG improves the performance of downstream analyses, including cell clustering and gene correlation.
HRG is also effective for selecting informative genes from spatial transcriptomic data.

Conclusions:

HRG offers a powerful and accurate approach for gene selection in scRNA-seq and spatial transcriptomic data.
This method enhances the reliability and interpretability of single-cell data analyses.
HRG provides a valuable tool for researchers in genomics and computational biology.