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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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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Partial domain adaptation enables cross domain cell type annotation between scRNA-seq and snRNA-seq.

Xiran Chen^1,2,3, Quan Zou², Qinyu Cai⁴

¹School of Computer and Artificial Intelligence, Shandong Jianzhu University, Shandong, China‌‌.

Plos Computational Biology

|May 6, 2026

Summary

ScNucAdapt enables accurate cell type annotation between single-nucleus (snRNA-seq) and single-cell (scRNA-seq) data. This method overcomes data differences, improving cross-dataset analysis for researchers.

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Last Updated: May 8, 2026

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Area of Science:

Single-cell genomics
Computational biology
Bioinformatics

Background:

Accurate cell type annotation is crucial for single-cell RNA sequencing (scRNA-seq) analysis.
Single-nucleus RNA sequencing (snRNA-seq) complements scRNA-seq, enabling analysis of frozen tissues and rare cell types.
Cross-annotation between scRNA-seq and snRNA-seq datasets is challenging due to distinct data distributions and cell compositions.

Purpose of the Study:

To develop a novel computational method for cross-annotation between scRNA-seq and snRNA-seq datasets.
To address the technical and biological differences that hinder accurate cell type identification across these data types.
To provide a robust framework for integrating and analyzing diverse single-cell and single-nucleus data.

Main Methods:

Introduction of ScNucAdapt, a method specifically designed for cross-annotation between paired and unpaired scRNA-seq and snRNA-seq data.
Implementation of partial domain adaptation techniques to reconcile distributional and cell composition discrepancies between datasets.
Validation of ScNucAdapt performance on both paired and unpaired experimental datasets.

Main Results:

ScNucAdapt demonstrates robust and accurate cell type annotation capabilities when applied to cross-dataset analysis.
The method significantly outperforms existing approaches in cross-annotation tasks involving scRNA-seq and snRNA-seq data.
Experimental results confirm the effectiveness of ScNucAdapt in handling distributional and compositional variations.

Conclusions:

ScNucAdapt offers a practical and effective solution for cross-domain cell type annotation between scRNA-seq and snRNA-seq data.
The developed framework enhances the utility of single-cell and single-nucleus sequencing by enabling integrated analysis.
ScNucAdapt advances the field of single-cell genomics by facilitating more comprehensive cell type identification across different experimental contexts.