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

• README.md --> Markdown file
• makefile --> main script
• generate_files.pl --> script to generate the necessary files. This script uses the original datasets files (with no filter for unconfident gene-tissue associations)
• add_orthology_info.pl --> 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"] <- "http://cran.us.r-project.org"
          options(repos=r)})

3. Download the datasets from https://doi.org/10.6084/m9.figshare.5715880 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

./data/

• 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

Requirements

• Perl
• R