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<p>Cytometry-based single-cell proteomics (SCP) has emerged as a powerful technique that greatly advances our understanding of complex biological systems with a new level of granularity. Various methods have been developed to process cytometry-based SCP data. However, it remains extremely challenging to identify the well-performing processing workflows for specific datasets. Here, we develop ANPELA, an out-of-the-box method for navigating the proteomic data processing based on large-scale screening. It enables a comparison among the performances of thousands of the processing workflows in identifying cell subpopulations and inferring pseudo-time trajectories based on machine learning. Several cases are then analyzed, highlighting its ability to identify the optimal ways of data processing for cytometry-based SCP studies. A new package is also deployed to ensure multiscenario usability (such as desktop software, R package and online server), data security (enabling local and open-source execution) and a user-friendly interface (realizing interactive and visualizable applications). Overall, ANPELA can be utilized by a broad audience, including those without coding skills, and is freely accessible and downloadable at <a href="https://idrblab.org/anpela/">https://idrblab.org/anpela/</a>. Its execution time may range from minutes to hours depending on the size of the analyzed data.</p>

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Navigating the data processing for cytometry-based single-cell proteomics

  • 期刊论文

  • 发表日期：

    2025年10月16日

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  • Huaicheng Sun,
  • Yuan Zhou,
  • Ruoyu Jiang,
  • Yuxuan Liu,
  • Chengbin Gu,
  • Ziqi Pan,
  • Minjie Mou,
  • Xichen Lian,
  • Bohan Chen,
  • Tianle Niu,
  • Ying Zhang,
  • Yintao Zhang,
  • Baoliang Zhang,
  • Xiuna Sun,
  • Hao Yang,
  • Xin Shen,
  • Yangbo Dai,
  • Jiannan Deng,
  • Siqi Liu,
  • Yang Zhang,
  • Mang Xiao,
  • Wanqing Xie,
  • Qingxia Yang,
  • Tingting Fu,
  • Feng Zhu

摘要

Cytometry-based single-cell proteomics (SCP) has emerged as a powerful technique that greatly advances our understanding of complex biological systems with a new level of granularity. Various methods have been developed to process cytometry-based SCP data. However, it remains extremely challenging to identify the well-performing processing workflows for specific datasets. Here, we develop ANPELA, an out-of-the-box method for navigating the proteomic data processing based on large-scale screening. It enables a comparison among the performances of thousands of the processing workflows in identifying cell subpopulations and inferring pseudo-time trajectories based on machine learning. Several cases are then analyzed, highlighting its ability to identify the optimal ways of data processing for cytometry-based SCP studies. A new package is also deployed to ensure multiscenario usability (such as desktop software, R package and online server), data security (enabling local and open-source execution) and a user-friendly interface (realizing interactive and visualizable applications). Overall, ANPELA can be utilized by a broad audience, including those without coding skills, and is freely accessible and downloadable at https://idrblab.org/anpela/. Its execution time may range from minutes to hours depending on the size of the analyzed data.