This application provides an interactive user interface for the R package tidyproteomics. Users may upload their protein- or peptide-level data for abundance subsetting, contaminant removal, abundance normalization, differential expression analysis, and ontology enrichment.
Please proceed to the 'Data Input and Summary' tab to begin your analysis. The analysis options available for your dataset will populate the sidebar after you have uploaded your data.
Data input and information
The current page describes all the features contained in the application.
Data input and summary
Users may upload an Excel file containing peptide- or protein-level data outputs from Proteome Discoverer and MaxQuant searches. After the file is processed into a tidyproteomics object, the user may view its attributes, such as annotation information, sample identifiers, and raw protein abundances.
Data subsetting and summary
Users may subset their data by any of the qualitative or quantitative variables present across the experiments, accounting, or annotation attributes of the data set. Users may also specify a text pattern for removing contaminant proteins from their data. Finally, sample groups can also be reassigned or renamed using the interactive spreadsheet.
Once data are uploaded and optionally subsetted, users can specify a method for imputing missing protein abundance values. Imputation can be specified to occur before or after protein abundance normalization, which can be performed using the methods chosen by the user in this tab.
This tab appears once peptide-level data are uploaded to the application. Peptide abundances are transformed into protein abundances by using the collapse function of tidyproteomics. Please note that the collapse of normalized peptide abundances into protein-level data is not currently supported. The unprocessed dataset will be used if the data are not modified in the data subsetting tab.
Users may conduct differential expression analysis using the preprocessed protein abundance values by specifying sample groups for comparison and a statistical method for estimating fold changes and p-values. Results are displayed in an interactive volcano or proportional plot and an accompanying table.
This tab appears after conducting differential expression analysis. Users may select a group comparison and examine the differential expression data for enrichment of terms from the user-specified ontology. Users can specify either the GSEA algorithm or a Wilcoxon rank sum comparison for conducting this analysis.
Copyright © 2023, California Institute of Technology (Caltech), based on support from the Institute for Collaborative Biotechnologies through cooperative agreement W911NF-19-2-0026 from the U.S. Army Research Office. The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.
All rights reserved.
All rights reserved.
tidyproteomics object summary
Abundance CVs and dynamic range
Principal component analysis (PCA)
Differential expression parameters
Volcano plot / Expression plot
Differential expression table
Annotation enrichment plot
Annotation enrichment table