Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

March 11, 2024 | Yi Hsiao, Haijian Zhang, Ginny Xiaohe Li, Yamei Deng, Fengchao Yu, Hossein Valipour Kahrood, Joel R. Steele, Ralf B. Schittenhelm, and Alexey I. Nesvizhskii
The article introduces FragPipe-Analyst, a user-friendly downstream analysis tool for quantitative proteomics data generated by the FragPipe computational platform. FragPipe-Analyst is designed to support major quantification workflows, including label-free quantification (LFQ), tandem mass tags (TMT), and data-independent acquisition (DIA). It offers a range of functionalities such as missing value imputation, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To enhance advanced analysis and customized visualizations, an R package called FragPipeAnalystR is developed, which supports site-specific analysis of post-translational modifications (PTMs). The article also provides detailed methods for data processing, including normalization, imputation, and statistical analysis. Several datasets, including TMT-based ccRCC cancer proteomics data, DIA-based ccRCC cancer proteomics data, LFQ AP-MS data, and LiP-MS data, are used to demonstrate the capabilities of FragPipe-Analyst and FragPipeAnalystR. The results show that these tools effectively support the interpretation of complex proteomics data, providing insights into biological differences between conditions and facilitating reproducible research.The article introduces FragPipe-Analyst, a user-friendly downstream analysis tool for quantitative proteomics data generated by the FragPipe computational platform. FragPipe-Analyst is designed to support major quantification workflows, including label-free quantification (LFQ), tandem mass tags (TMT), and data-independent acquisition (DIA). It offers a range of functionalities such as missing value imputation, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To enhance advanced analysis and customized visualizations, an R package called FragPipeAnalystR is developed, which supports site-specific analysis of post-translational modifications (PTMs). The article also provides detailed methods for data processing, including normalization, imputation, and statistical analysis. Several datasets, including TMT-based ccRCC cancer proteomics data, DIA-based ccRCC cancer proteomics data, LFQ AP-MS data, and LiP-MS data, are used to demonstrate the capabilities of FragPipe-Analyst and FragPipeAnalystR. The results show that these tools effectively support the interpretation of complex proteomics data, providing insights into biological differences between conditions and facilitating reproducible research.
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Understanding Analysis and visualization of quantitative proteomics data using FragPipe-Analyst