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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: Jun 7, 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

scHDeepInsight: a hierarchical deep learning framework for precise immune cell annotation in single-cell RNA-seq

Shangru Jia¹, Artem Lysenko², Keith A Boroevich³

¹Laboratory for Medical Science Mathematics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

Briefings in Bioinformatics

|October 9, 2025

Summary

This summary is machine-generated.

Accurate immune cell classification is vital for understanding health and disease. scHDeepInsight, a new deep learning tool, enhances single-cell RNA sequencing analysis by precisely identifying diverse immune cell subtypes.

Keywords:

cell annotation cell subtype deep learning single-cell RNA sequencing transformers

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Area of Science:

Immunology
Computational Biology
Genomics

Background:

Accurate immune cell classification is essential for understanding cellular roles in health and disease.
Single-cell RNA sequencing (scRNA-seq) data presents challenges due to complex immune cell hierarchies.
Existing methods struggle with high-resolution immune cell subtype identification.

Purpose of the Study:

To develop an advanced deep learning framework, scHDeepInsight, for precise immune cell classification.
To improve the accuracy and resolution of immune cell subtype identification from scRNA-seq data.
To leverage biologically-informed architectures and adaptive loss functions for enhanced classification.

Main Methods:

scHDeepInsight converts gene expression data into 2D images for convolutional neural network analysis.
It employs a biologically-informed classification architecture with adaptive hierarchical focal loss (AHFL).
The framework utilizes hierarchical relationships among immune cell types for improved classification accuracy.

Main Results:

scHDeepInsight achieved an average accuracy of 93.2% across seven diverse tissue datasets, outperforming current methods by 5.1%.
The model accurately distinguished 50 distinct immune cell subtypes, including rare and closely related ones.
SHAP-based interpretability quantified gene contributions, revealing biological underpinnings of classification.

Conclusions:

scHDeepInsight offers a robust solution for high-resolution immune cell subtype characterization.
The framework is well-suited for detailed immunological profiling and adaptable to non-immune cell types.
This tool advances the analysis of complex scRNA-seq data for immunological research.