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OmicScope: from quantitative proteomics to systems biology.

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Documentation Status PyPI Package latest release PyPI Wheel Supported versions Supported implementations Commits since latest release

OmicScope: OmicScope: from quantitative proteomics to systems biology.

OmicScope is a comprehensive workflow designed to analyze and provide insights on quantitative proteomics data. To date, OmicScope works with data generated from Progenesis QI for Proteomics, MaxQuant, PatternLab V, and DIA-NN. Additionally, a fourth generic input can be used, enabling users to run OmicScope with data from different platforms, such as transcriptomics. For users that previously performed statistical analysis, OmicScope provides the Snapshot method to quickly import data.

OmicScope can perform differential expression analysis in both static and longitudinal experimental designs. For static experiments, proteins that are differentially regulated are determined by t-tests (for 2 group comparison) or One-way ANOVA (for >2 group comparison); while for longitudinal analysis, OmicScope performs the pipeline suggested by Storey, 2005.

Once the differential expression data is obtained, the user can perform Over-Representation Analysis (ORA) or Gene-Set Enrichment Analysis (GSEA), which are implemented according to GSEApy in the EnrichmentScope module.

Both differential expression and enrichment analyses have a comprehensive visualization toolkit generated by OmicScope, including dotplots, networks, heatmaps, etc.

Finally, for each experiment performed by OmicScope, it is possible to export an omics file (.omics extension) for further use in the Nebula workflow. Nebula is a specialized module that enables analysis of multiple studies or comparisons, providing several types of analysis to compare those groups and find similarities among them.



pip install omicscope

You can also install the in-development version with:

pip install



1.0.0 (2022-11-10)

  • First release on PyPI.

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