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单细胞蛋白质组学数据的复制揭示了重要的计算挑战。

Replication of single-cell proteomics data reveals important computational challenges.

机构信息

Computational Biology and Bioinformatics Unit (CBIO), de Duve Institute, UCLouvain, Belgium.

出版信息

Expert Rev Proteomics. 2021 Oct;18(10):835-843. doi: 10.1080/14789450.2021.1988571. Epub 2021 Oct 25.

Abstract

INTRODUCTION

Mass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses.

AREAS COVERED

This special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses.

EXPERT OPINION

Replicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges.

摘要

简介

基于质谱的蛋白质组学正在积极采用定量、单细胞水平分析。事实上,样品制备和质谱(MS)的最新进展使得基于定量 MS 的单细胞蛋白质组学(SCP)得以出现。虽然令人兴奋和有前途,但 SCP 仍有许多不足之处。目前的分析工作流程是自定义的,从头开始构建。因此,该领域迫切需要标准化的软件,以促进有原则和可重复的 SCP 数据分析。

涵盖领域

本特别报告是 SCP 数据分析形式化和标准化的第一步。scp 是伴随这项工作的软件,成功复制了一项具有里程碑意义的 SCP 研究,并适用于其他实验和设计。我们创建了一个包含复制工作流程的存储库,并提供了全面的文档,以促进 SCP 数据分析的进一步传播和改进。

专家意见

复制 SCP 数据分析揭示了 SCP 数据分析中的重要挑战。我们详细描述了其中两个挑战:批次校正和数据缺失。我们提供了当前的最新技术,并说明了相关的局限性。我们还强调了批次效应和数据缺失之间存在的密切关系,并提供了处理这些令人兴奋的挑战的途径。

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