Benchmarking informatics workflows for data-independent acquisition single-cell proteomics.

ABSTRACT: Recent years have seen a rise of single-cell proteomics by data-independent acquisition mass spectrometry (DIA MS). While diverse data analysis strategies have been reported in literature, their impact on the outcome of single-cell proteomic experiments has been rarely investigated. Here, we present a framework for benchmarking data analysis strategies for DIA-based single-cell proteomics. This framework provides a comprehensive comparison of popular DIA data analysis software tools and searching strategies, as well as a systematic evaluation of method combinations in subsequent informatic workflow, including sparsity reduction, missing value imputation, normalization, batch effect correction, and differential expression analysis. Benchmarking on simulated single-cell samples consisting of

SUBMITTER: Wang J 

PROVIDER: S-EPMC12639053 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature