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ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.

Ying Zhang1, Huaicheng Sun1, Xichen Lian1

  • 1College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

ANPELA 2.0 enhances single-cell proteomics (SCP) analysis by offering over 1000 quantification workflows. This tool systematically evaluates and ranks workflows using machine learning for optimal data analysis and biological insights.

Keywords:
cell population identificationcomprehensive assessmentparallel computingprotein quantificationsingle-cell proteomicstrajectory inference

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Area of Science:

  • Proteomics
  • Single-cell analysis
  • Bioinformatics

Background:

  • Traditional bulk proteomics overlooks cellular heterogeneity.
  • Single-cell proteomics (SCP) captures this heterogeneity but faces quantification challenges.
  • Selecting optimal SCP quantification workflows is difficult due to numerous options and dataset-specific performance.

Purpose of the Study:

  • To address the challenge of selecting optimal quantification workflows for single-cell proteomics (SCP) data.
  • To develop an enhanced and accelerated tool for in-depth and high-quality SCP data quantification.
  • To provide a systematic approach for evaluating and identifying the best-performing SCP quantification workflows.

Main Methods:

  • Updated ANPELA to version 2.0, incorporating over 1000 quantification workflows.
  • Implemented machine learning for systematic, multi-perspective performance evaluation of workflows.
  • Utilized overall performance ranking and parallel computation to identify optimal workflows.

Main Results:

  • ANPELA 2.0 provides the most comprehensive set of quantification alternatives available.
  • The tool enables systematic evaluation and identification of optimal workflows based on performance.
  • Extensive validation demonstrated the tool's effectiveness on benchmark datasets and application scenarios.

Conclusions:

  • ANPELA 2.0 significantly enhances the analysis of single-cell proteomics (SCP) data.
  • The tool facilitates the selection of optimal workflows for accurate and reliable biological insights.
  • ANPELA 2.0 has great application potential in current SCP research.