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TISSUES 2.0: An integrative web resource on mammalian tissue expression

Code to reproduce the fold enrichment analyses and figures from the article

Project structure

  • --> Markdown file
  • makefile --> main script
  • --> script to generate the necessary files. This script uses the original datasets files (with no filter for unconfident gene-tissue associations)
  • --> script to generate files containing scored gene-tissue pairs and 1:1 orthologs with the other three organisms, if these exist, for each gene.
  • analyses.R --> script orchestrating the generation of the figures
  • R/ --> All the scripts necessary to reproduce the figures
    • summary_figure_drawing.R --> Initial figure with the tissues covered by each dataset
    • fold_enrichment_analysis_per_dataset.R --> Generates the fold-enrichment plots for each dataset
    • fold_enrichment_score_calibration_analysis.R --> Score calibration figure
    • correlation_analysis.R --> Heatmap figures based on Pearson's correlation coefficients between final star scores for gene tissue pairs across datasets
    • functions.R --> functions used across the other four scripts
  • data/
  • datasests/ --> Datasets files need to be stored here
  • dictionary/ --> Contains the files necessary to perform the tissue backtracking bassed on BRENDA Ontology
  • labels.tsv --> The bto terms corresponding to the 21 tissues of interest: tissues_code tissue_name BTO
  • bto_entities.tsv --> mapping of bto terms to internal identifiers: internal_code tissues_code BTO
  • bto_groups .tsv --> the parent-children relationships used to do the backtracking: internal_code parent_internal_code
  • orthology/ --> Contains the eggNOG orthology groups files necessary to extract 1:1 orthologs across organisms
  • roNOG.tsv --> ortholgy groups for rodents, used for extracting 1:1 orthologs between mouse and rat
  • maNOG.tsv --> ortholgy groups for mammals, used for extracting orthologs between the other organism pairs
  • figures/ --> Folder where all the figures generated are stored

Run the analyses

  1. Download the project

  2. Make sure you have a default CRAN repository set, for example by putting the following into your ~/.Rprofile:

    local({r <- getOption("repos")
           r["CRAN"] <- ""
  3. Download the datasets from and place them in the data/datasets folder

  4. Execute the makefile script from the command line: > make

  5. All the files will be generated in the data folder

  6. All the figures from the analyses will be created in the figures/ folder

Generated files


  • Fold enrichment analyses result files: DATASET_uniprot_fold_enrichment_analysis.tsv
  • Scored gene-tissue pairs for the 21 major tissues: pairs_major_tissues.tsv ./figures/
  • Summary of covered tissues: Summary_figure_tissues.png
  • Fold enrichment plots: datasets_fold_enrichment.png
  • Score calibration plot: datasets_score_calibration.png
  • Heatmaps with Pearson's correlation scores between pairs of datasets


  • Perl
  • R


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