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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Jun 21, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Target-Oriented Reference Construction for supervised cell-type identification in scRNA-seq.

Xin Wei¹, Wenjing Ma², Zhijin Wu¹

¹Brown University.

Research Square

|July 9, 2024

Summary

Supervised cell-type identification in single-cell RNA sequencing (scRNA-seq) benefits from a new strategy called Target-Oriented Reference Construction (TORC). TORC improves accuracy by creating better reference datasets tailored to the target data.

Keywords:

Cell-type identification Reference construction Supervised learning scRNA-seq

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Last Updated: Jun 21, 2025

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

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Published on: January 10, 2019

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate cell-type identification is essential for single-cell RNA sequencing (scRNA-seq) data analysis.
Supervised methods offer high accuracy and efficiency but depend heavily on reference dataset quality.
Existing tools lack standardized methods for selecting and constructing optimal reference datasets.

Purpose of the Study:

To introduce Target-Oriented Reference Construction (TORC), a novel strategy for building reference datasets in scRNA-seq.
To address the challenge of data distribution and cell-type composition discrepancies between reference and target datasets.
To enhance the performance of supervised cell-type identification in scRNA-seq.

Main Methods:

TORC is a widely applicable strategy for constructing reference datasets.
The method specifically targets the construction of references based on a given target dataset.
TORC aims to alleviate differences in data distribution and cell-type composition.

Main Results:

Extensive benchmarks on simulated and real scRNA-seq data were performed.
TORC demonstrated consistent improvements in cell-type identification accuracy.
The strategy proved effective in aligning reference and target data characteristics.

Conclusions:

TORC provides a robust solution for reference dataset construction in supervised scRNA-seq analysis.
The method enhances the reliability and accuracy of cell-type identification.
TORC is freely available, promoting its adoption in the research community.