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Updated: Sep 13, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

Han Ji¹, Xinwei He¹, Hongwei Li²

¹School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan, 430074, China.

BMC Bioinformatics

|July 30, 2025

Summary

This summary is machine-generated.

CYCLONE, a novel method using recycle contrastive learning, effectively integrates single-cell gene expression data. It enhances cell clustering accuracy by removing batch effects while preserving crucial batch-specific cell types.

Keywords:

Batch effect Integration Recycle contrastive learning scRNA-seq

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches is crucial for robust analysis.
Batch effects can confound cellular identity and function, necessitating effective integration methods.

Purpose of the Study:

To introduce CYCLONE, a novel method for single-cell gene expression data integration.
To address the challenge of batch effect removal while preserving biological variation.

Main Methods:

CYCLONE employs a recycle contrastive learning network combined with a Variational Autoencoder (VAE).
It iteratively refines low-dimensional representations and MNN (mutual nearest neighbor) pairs for improved data integration.
KNN (k-nearest neighbor) pairs are augmented to identify and retain batch-specific cell types.

Main Results:

CYCLONE demonstrated improved clustering accuracy on simulated and real scRNA-seq datasets.
The method effectively eliminated batch effects.
CYCLONE successfully preserved batch-specific cell types, avoiding overcorrection.

Conclusions:

CYCLONE is an effective integration method based on recycle contrastive learning.
It enhances cell clustering accuracy and batch effect removal.
The method preserves critical batch-specific information.