GEDA: Gene Expression & Drug Activity in Cancer Cells: Robust Correlations & Networks

Gene Graph

Click to see this DATA as TABLE

Drug Graph

Click to see this DATA as TABLE


  1. Select the dataset to be used (currently only FDA is supported).
  2. Select to filter by gene or drug.
  3. Select the desired identifier.
  4. Select the cell line/s to plot (all cell lines by default). Aditional information in the "Cell lines Info" button.
  5. Select if you want to highlight some cell line/s.

Graphics description

  • A. Graphic generated in the "Barplot" tab for the drug and the selected gene, with the corresponding p-values and correlations. In this tab there is a button on the top left that provides more information about the graphic.
  • B. Graphic generated in the "Scatterplot" tab for the drug and the selected gene, with the corresponding p-values and correlations. This graphic is interactive and shows the coordinates of the points.
  • C. "Data Table", table with the data included in the graphics. This table can be downloaded/exported in different formats with the buttons above the header.
  • D. Gene and Drug Graphs showing the associations reported by Drugbank and in the present study. The data used can be accessed in table format by clicking on the blue button at the bottom.

92 FDA drugs reported in this work and their known targets according to DrugBank ¹.

[1] All data shown in the tables above has been obtained from DrugBank. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2017 Nov 8. doi: 10.1093/nar/gkx1037.

363 genes reported in this work.

GEDA (Gene Expression & Drug Activity in Cancer Cells: Robuts Correlations & Networks) is a web tool developed in R-shiny to plot Gene expression versus Drug activity in different cell lines grouped by tissues. GEDA uses Pearson & Spearman correlation to put forth gene & drug interaction grouped by tissues in two different drug datasets (FDA and Non-FDA approved), further evaluated by the p-value of each correlation.
Creative Commons License GEDA is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

R packages used

[1] W. Chang, J. Cheng, J. Allaire, et al. shiny: Web Application Framework for R. R package version 1.4.0. 2019. <URL:>.

[2] M. Dowle and A. Srinivasan. data.table: Extension of data.frame. R package version 1.12.2. 2019. <URL:>.

[3] V. Perrier, F. Meyer, and D. Granjon. shinyWidgets: Custom Inputs Widgets for Shiny. R package version 0.4.8. 2019. <URL:>.

[4] T. Rinker and D. Kurkiewicz. pacman: Package Management Tool. R package version 0.5.1. 2019. <URL:>.

[5] P. Solymos and Z. Zawadzki. pbapply: Adding Progress Bar to ’apply’ Functions. R package version 1.4-1. 2019. <URL:>.

[6] H. Wickham, W. Chang, L. Henry, et al. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. R package version 3.2.1. 2019. <URL:>.

[7] H. Wickham, R. François, L. Henry, et al. dplyr: A Grammar of Data Manipulation. R package version 0.8.3. 2019. <URL:>.

[8] Y. Xie, J. Cheng, and X. Tan. DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.8. 2019. <URL:>.

[9] G. Yu. shadowtext: Shadow Text Grob and Layer. R package version 0.0.5. 2019. <URL:>.

[10] D. Attali and T. Edwards. shinyalert: Easily Create Pretty Popup Messages (Modals) in ‘Shiny’. R package version 1.0. 2018. <URL:>.

[11] S. Garnier. viridis: Default Color Maps from ‘matplotlib’. R package version 0.5.1. 2018. <URL:>.

[12] H. Wickham. reshape: Flexibly Reshape Data. R package version 0.8.8. 2018. <URL:>.

[13] B. Auguie. gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.3. 2017. <URL:>.

[14] A. Sali. shinycssloaders: Add CSS Loading Animations to ‘shiny’ Outputs. R package version 0.2.0. 2017. <URL:>.

[15] H. Wickham. tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. 2017. <URL:>.


- Presented in X Int. Conf. on Bioinformatics SoIBio Conference 2019 Montevideo, URUGUAY. Seeking new target candidates in cancer: Analysis of the correlation of Oncogene expression with Drug activity. ST: Mónica M. Arroyo. Pontifical Catholic University of Puerto Rico (PCUPR), Ponce, Puerto Rico.
- Monica M Arroyo, Alberto Berral-Gonzalez, Santiago Bueno-Fortes, Diego Alonso-Lopez, Javier De Las Rivas. Discovery of new drug-target candidates in cancer: analysis of correlation of cancer gene expression with drug activity. Biomolecules. Submitted for Publication .

About us

The Bioinformatics and Functional Genomics Research Group is located in the Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL) Salamanca (SPAIN) directed by Dr. Javier De Las Rivas. Our research is driven by the integrative analyses of genomic and proteomic data derived from patient-oriented studies in cancer. Using bioinformatics applied to find new cancer biomarkers and cancer drivers, cancer genomic data to improve patient stratification, disease subtype profiling and prognosis analysis, computational Biology and Bioinformatics applied to studies in onco-hematology and studies on the human proteome and interactome: build, validate and analyse protein interaction networks.

GEDA v 1.0.0.

Browsers tested: Chrome ( v.80 & v.76), Firefox (v.72 & v.66), Opera( v.66 & v.60), Safari(v.12, v.11 & v10.1), Internet Explorer(v.11, v.10 & v.8)

Tested in:

  • macOS
    • High Sierra
    • Mojave
    • Sierra
  • Windows
    • 10
    • 8.1
    • 8
    • 7
    • XP
  • Linux
    • Ubuntu 18.04
Email suggestions or bugs to Alberto Berral-Gonzalez


Bioinformatics and Functional Genomics Research Group
Cancer Research Center (CiC-IBMCC, CSIC/USAL)
Salamanca - Spain

GEDA is a tool based on the work done by Rajapakse VN, Luna A, Yamade M et al. iScience 10: 247-264 (2018) and CellMinerCDB.
GEDA logo created based on the work of LAFS, Mello, RULI from Noun Project.