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prolfquapp - an R package to report differential expression analysis results.

2022-12-21

Source: vignettes/prolfquapp.Rmd
prolfquapp.Rmd

R Markdown

To ease the usage barriers of the R package to users not familiar with statistics and R programming, we developed an application based on the prolfqua package into our data management platform B-Fabric [@bfabric;@bfabricjib2022]. The B-Fabric system runs a computing infrastructure controlled by a local resource management system that supports cloud-bursting [@VMMAD]. This integration enables users to select the input data and basic settings in a graphical user interface (GUI).

In this GUI application, we determine the model formula, and which comparisons to compute from the sample annotation provided in tabular form, similarly to SAINTexpress or MSstats. However, this application can analyze only parallel group designs with and without repeated measurements or factorial designs without interactions. The user receives a report including input files, the R markdown file, and R scripts necessary to replicate the analysis using their in-house R installation. In this way, prolfqua and B-Fabric help scientists to meet requirements from funding agencies, journals, and academic institutions while publishing their data according to the FAIR [@FAIR] data principles. We are working on creating a shiny stand-alone application with the described functionality and making it available soon.

Describe inputs

Describe annotation table format:

  • table for parallel group design
  • table for paired analysis
  • table for factorial analysis
  • additional column with explicit contrasts specification

Describe yaml file with application parameters.

  • show example shiny application which can be used to generate yaml file, validate inputs and run the analysis

Describe generated ouptuts

Describe html document produced:

  • Introduction:
  • B-fabric related information
  • Results
    • Peptide and Protein identification
    • Missing Value Analysis
    • Protein Abundance Analysis
  • Differential Expression Analysis
    • Differentially Expressed Proteins
  • Additional Analysis

Describe additional files

The zip file contains an excel file Results.xlsx. All the figures can be recreated using the data in the excel file. The Excel file contains the following spreadsheets:

  • annotation - the annotation of the samples in the experiment
  • raw_abundances table with empirical protein abundances.
  • normalized_abundances table with normalized protein abundances.
  • raw_abundances_matrix A table where each column represents a sample and each row represents a protein and the cells store the empirical protein abundances.
  • normalized_abundances_matrix A table where each column represents a sample and each row represents a protein and the cells store the empirical protein abundances.
  • diff_exp_analysis A table with the results of the differential expression analysis. For each protein there is an row containing the estimated difference between the groups, the false discovery rate FDR, the 95% confidence interval, the posterior degrees of freedom.
  • missing_information - spreadsheet containing information if a protein is present (1) or absent in a group (0).
  • protein_variances - spreadsheet which for each protein shows the variance (var) or standard deviation (sd) within a group, the number of samples (n) and the number of observations (not_na) as well as the group average intensity (mean).

The zip file also includes an “boxplot.pdf” file which shows the boxplots of normalized protein abundances for all the proteins in the dataset.

The data can be used to perform functional enrichment analysis [@monti2019proteomics]. To compare the obtained results with known protein interactions we recommend the string-db.org [@Szklarczyk2017], which is a curated database of protein-protein interaction networks for a large variety of organisms. To simplify the data upload to string-db we include text files containing the uniprot ids:

  • ORA_background.txt all proteins.
  • ORA_<contrast_name>.txt proteins accepted with the FDR and difference threshold.

Other web applications allowing to run over representation analysis (ORA) [@monti2019proteomics] are:

Furthermore Protein IDs sorted by t-statistic can then be subjected to gene set enrichment analysis (GSEA) [@subramanian2005gene]. To simplify running GSEA we provide the file :

  • GSEA_<contrast_name>.rnk

This file can be used with the webgestalt web application or used with the GSEA application from gsea-msigdb

For questions and improvement suggestions, with respect to this report, please do contact .