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Related Experiment Video

Updated: Jun 3, 2025

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

Benchmark of Data Integration in Single-Cell Proteomics.

Yaguo Gong¹, Yangbo Dai², Qibiao Wu¹

¹School of Pharmacy, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macao 999078, China.

Analytical Chemistry

|January 6, 2025

Summary

This summary is machine-generated.

Integrating single-cell proteomics (SCP) data is challenging due to batch effects. This study benchmarks methods, recommending ComBat, Scanorama, and Seurat v3 CCA for effective SCP data integration.

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Last Updated: Jun 3, 2025

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Published on: April 19, 2019

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

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Published on: November 17, 2019

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

Published on: December 9, 2022

Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Single-cell proteomics (SCP) data often contains batch-specific variations due to processing differences and biases.
Effective integration of SCP data is crucial for preserving biological insights while removing unwanted technical effects.

Purpose of the Study:

To benchmark popular data integration methods for single-cell proteomics (SCP).
To propose a novel evaluation system for assessing SCP data integration performance.
To identify the most suitable methods for integrating SCP data.

Main Methods:

Benchmarking analysis of existing data integration methods.
Development of a novel evaluation system with three objective measures: batch effect correction, biological variance conservation, and marker identification.
Utilization of five diverse benchmark datasets covering various scenarios (protein/cell numbers, batches, cell types, data balance).

Main Results:

ComBat, Scanorama, and Seurat version 3 CCA were identified as the top-performing methods.
The proposed evaluation system provides a comprehensive assessment from multiple perspectives.
The study successfully identified recommended methods for SCP data integration.

Conclusions:

ComBat, Scanorama, and Seurat v3 CCA are recommended for single-cell proteomics data integration.
This systematic evaluation offers valuable guidance for researchers selecting integration methods.
Addressing batch effects while preserving biological variance is key for robust SCP analysis.