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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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iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data

Weihua Zheng¹, Wenwen Min¹, Shunfang Wang¹

¹Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650500, China.

Bioinformatics (Oxford, England)

|July 21, 2025

Summary

This summary is machine-generated.

Interpretable Graph Contrastive Learning with Adaptive Positive Sampling (IGCLAPS) enhances single-cell RNA sequencing (scRNA-seq) analysis by improving cell clustering and revealing gene expression patterns.

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Last Updated: Sep 14, 2025

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

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Published on: April 30, 2011

Identification of Circular RNAs using RNA Sequencing

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

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Published on: December 1, 2023

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) provides high-resolution biological insights.
Cell clustering is vital for understanding cell heterogeneity in scRNA-seq data.
Existing methods struggle to fully leverage cell-to-cell relationships.

Purpose of the Study:

To introduce a novel end-to-end graph contrastive clustering method for scRNA-seq data.
To enhance the utilization of cell relationships in scRNA-seq analysis.
To develop an interpretable clustering approach.

Main Methods:

Proposing Interpretable Graph Contrastive Learning with Adaptive Positive Sampling (IGCLAPS).
Utilizing a graph transformer for low-dimensional embedding.
Employing a dual-head graph contrastive learning module for simultaneous dimension reduction and clustering.
Developing an adaptive positive sampling module based on expression similarity and soft cluster labels.

Main Results:

IGCLAPS effectively enhances cell clustering performance in scRNA-seq data.
The method generates interpretable gene expression patterns.
Experiments demonstrate improved clustering, visualization, and differential expression analysis.

Conclusions:

IGCLAPS offers a powerful and interpretable approach for scRNA-seq data analysis.
The adaptive positive sampling strategy improves contrastive learning accuracy.
This method advances the field of single-cell data interpretation.