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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 microarray-based...

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Updated: May 11, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

scRCA: A Siamese network-based pipeline for annotating cell types using noisy single-cell RNA-seq reference data.

Yan Liu¹, Chen Li², Long-Chen Shen³

¹Department of Computer Science, Yangzhou University, Yangzhou, 225100, China.

Computers in Biology and Medicine

|March 30, 2025

Summary

This summary is machine-generated.

This study introduces scRCA, a novel computational pipeline for accurate cell type annotation in single-cell RNA sequencing data, even when reference datasets contain errors. scRCA outperforms existing methods and offers interpretability for biological insights.

Keywords:

Cell type annotation Interpretability Marker genes identification Noisy reference datasets Single-cell transcriptomic data

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Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Accurate cell type annotation is crucial for single-cell RNA sequencing (scRNA-seq) data analysis.
Existing methods struggle with noisy or error-containing reference datasets, limiting insights into cell heterogeneity and states.

Purpose of the Study:

To develop a computational pipeline for high-quality cell type annotation using noisy reference data.
To address the challenge of annotation errors in reference datasets for scRNA-seq analysis.

Main Methods:

Developed scRCA, a Siamese network-based pipeline for cell type annotation.
Integrated an interpreter module to explain model predictions and assess annotation reliability.

Main Results:

scRCA demonstrated superior performance across 14 datasets compared to existing reference-based annotation methods.
The pipeline successfully distinguished cancerous cells in a multiple myeloma dataset and identified key associated genes.

Conclusions:

scRCA provides a robust and practical solution for accurate cell type annotation from noisy reference data.
The interpretable nature of scRCA aids in discovering biologically relevant genes and facilitates clinical applications.