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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data.

Jiawei Zou^1,2, Fulan Deng³, Miaochen Wang⁴

¹School of Life Sciences and Biotechnology, Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China.

Briefings in Bioinformatics

|May 22, 2022

Summary

Differential expression gene detection in single-cell RNA sequencing data is crucial. We introduce a new R package that optimizes this process by considering gene filtering for improved accuracy.

Keywords:

differentially expressed gene detection evaluation gene filtering scRNA-seq data

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Differential expression (DE) gene detection is vital for analyzing single-cell RNA sequencing (scRNA-seq) data.
The impact of gene filtering on DE method performance remains undemonstrated.
Optimal DE detection strategies vary across scRNA-seq datasets, necessitating data-specific approaches.

Purpose of the Study:

To address the lack of gene filtering consideration in existing DE detection tools for scRNA-seq.
To develop data-specific DE gene detection strategies.
To introduce novel metrics for evaluating DE method performance on experimental datasets.

Main Methods:

Development of the R package 'single-cell Consensus Optimization of Differentially Expressed gene detection' (scCODE).
Introduction of two new metrics for evaluating DE gene detection performance.
Automatic optimization of DE gene detection tailored to individual scRNA-seq datasets.

Main Results:

scCODE provides a framework for optimizing DE gene detection in scRNA-seq.
The package incorporates gene filtering into the DE analysis pipeline.
New metrics enable better assessment of DE method suitability for specific datasets.

Conclusions:

Gene filtering significantly influences DE gene detection outcomes in scRNA-seq.
scCODE offers a robust, data-driven solution for optimizing DE analysis.
This approach enhances the biological insights derived from scRNA-seq experiments.