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

作者全名："Zhang, Ying; Sun, Huaicheng; Lian, Xichen; Tang, Jing; Zhu, Feng"

作者地址："[Zhang, Ying; Sun, Huaicheng; Lian, Xichen; Zhu, Feng] Zhejiang Univ, Affiliated Hosp 2, Coll Pharmaceut Sci, Sch Med, Hangzhou 310058, Peoples R China; [Tang, Jing] Chongqing Med Univ, Dept Bioinformat, Chongqing 400016, Peoples R China; [Zhu, Feng] Zhejiang Univ, Alibaba Zhejiang Univ Joint Res Ctr Future Digital, Innovat Inst Artificial Intelligence Med, Hangzhou 330110, Peoples R China"

通信作者："Zhu, F (通讯作者)，Zhejiang Univ, Affiliated Hosp 2, Coll Pharmaceut Sci, Sch Med, Hangzhou 310058, Peoples R China.; Zhu, F (通讯作者)，Zhejiang Univ, Alibaba Zhejiang Univ Joint Res Ctr Future Digital, Innovat Inst Artificial Intelligence Med, Hangzhou 330110, Peoples R China."

来源：ADVANCED SCIENCE

ESI学科分类：PHYSICS

WOS号：WOS:000955598800001

JCR分区：Q1

影响因子：14.3

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：cell population identification; comprehensive assessment; parallel computing; protein quantification; single-cell proteomics; trajectory inference

摘要："ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights."

基金机构："National Natural Science Foundation of China [81872798, U1909208]; Natural Science Foundation of Zhejiang Province [LR21H300001]; Leading Talent of the ""Ten Thousand Plan"" - National High-Level Talents Special Support Plan of China; Fundamental Research Fund for Central Universities [2018QNA7023]; Key R&D Program of Zhejiang Province [181201*194232101]; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare; Information Technology Center of Zhejiang University; ""Double Top-Class"" University Project; Westlake Laboratory (Westlake Laboratory of Life Sciences and Biomedicine); Alibaba Cloud; [2020C03010]"

基金资助正文：This study was supported by grants from the National Natural Science Foundation of China (81872798 and U1909208); the Natural Science Foundation of Zhejiang Province (LR21H300001); the Leading Talent of the "Ten Thousand Plan" - National High-Level Talents Special Support Plan of China; the Fundamental Research Fund for Central Universities (2018QNA7023); the "Double Top-Class" University Project (181201*194232101); the Key R & D Program of Zhejiang Province (2020C03010). This work was also supported by the Westlake Laboratory (Westlake Laboratory of Life Sciences and Biomedicine); the Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare; the Alibaba Cloud; the Information Technology Center of Zhejiang University.