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ToxCompl: Drugmatrix Completion

ToxCompl, or ToxCompl: Drugmatrix Completion, is a Shiny app developed in collaboration with the Oak Ridge National Laboratory to assist in predicting missing data in DrugMatrix1,2. This application is a companion to the following publication: doi:10.1101/2024.03.26.586669.

The app allows a user to view the gene expression, histopathology, clinical chemistry, and hematology information for the samples (chemical/dose/duration) in DrugMatrix. Gene expression data is mapped from rat gene to human gene using both the (Affymetrix) GeneChip® RG230 and the CodeLink RU1 microarrays.

The user may also enrich a set of genes pulled from the DrugMatrix samples using the Enrichr tool3,4,5.

We have also provided a cluster visualization in the app for the samples, split by each combination of tissue and microarray. To perform the clustering, we took the following steps:

  1. Split the matrix into combinations of tissue and microarray type (not all tissues are tested with both microarrays). The matrix contains the log10 ratio of treatment gene expression level vs. control gene expression level for each gene for each sample.
  2. For each row of each split matrix, scale the data:
    1. Save directionality of each log10 ratio.
    2. Rank genes in descending order by the absolute value of the log10 ratio.
    3. Assign the top 100 genes a rank from 100 (highest) to 1 (lowest). If two or more genes had the same log10 ratio, they were given the same ranking to have a maximum of 100 ranked genes; that is, if two genes are assigned rank 2, the next highest-ranked gene is assigned rank 4. Genes not in the top 100 are assigned a rank of 0. 2.4. Multiply each gene's directionality by its top 100 ranking.
  3. Perform dimension reduction using the Uniform Manifold Approximation and Projection (UMAP) package for Python6. Each chemical in these clusters are colored by their associated mode of action(s) as defined in ChEMBL26, 27, 28.

Additional software dependencies

name version citation
RStudio 2023.09.1 build 494 8
R 4.2.3 9
stats base 9
graphics base 9
grDevices base 9
utils base 9
datasets base 9
methods base 9
base base 9
visNetwork 2.1.2 10
stringr 1.5.0 11
shinyWidgets 0.7.6 12
shinyjs 2.1.0 13
shinycssloaders 1.0.0 14
RPostgres 1.4.4 15
rjson 0.2.21 16
enrichR 3.2 17
DT 0.28 18
dplyr 1.0.10 19
DBI 1.1.3 20
data.table 1.14.6 21
shiny 1.7.4.1 22
scrypt 0.1.6 23
shinymanager 1.0.410 24
tidyverse 2019 25

References

  1. https://ntp.niehs.nih.gov/data/drugmatrix
  2. https://github.com/NIEHS/DrugMatrix
  3. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013; 128(14).
  4. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016; gkw377 .
  5. Xie Z, Bailey A, Kuleshov MV, Clarke DJB., Evangelista JE, Jenkins SL, Lachmann A, Wojciechowicz ML, Kropiwnicki E, Jagodnik KM, Jeon M, & Ma’ayan A. Gene set knowledge discovery with Enrichr. Current Protocols, 1, e90. 2021. doi: 10.1002/cpz1.90
  6. https://doi.org/10.48550/arXiv.1802.03426
  7. Posit team (2023). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. http://www.posit.co/.
  8. R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
  9. Almende BV and Contributors, Thieurmel B (2022). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.1.2, https://CRAN.R-project.org/package=visNetwork.
  10. Wickham H (2022). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.5.0, https://CRAN.R-project.org/package=stringr.
  11. Perrier V, Meyer F, Granjon D (2023). shinyWidgets: Custom Inputs Widgets for Shiny. R package version 0.7.6, https://CRAN.R-project.org/package=shinyWidgets.
  12. Attali D (2021). shinyjs: Easily Improve the User Experience of Your Shiny Apps in Seconds. R package version 2.1.0, https://CRAN.R-project.org/package=shinyjs.
  13. Sali A, Attali D (2020). shinycssloaders: Add Loading Animations to a 'shiny' Output While It's Recalculating. R package version 1.0.0, https://CRAN.R-project.org/package=shinycssloaders.
  14. Wickham H, Ooms J, Müller K (2022). RPostgres: Rcpp Interface to PostgreSQL. R package version 1.4.4, https://CRAN.R-project.org/package=RPostgres.
  15. Couture-Beil A (2022). rjson: JSON for R. R package version 0.2.21, https://CRAN.R-project.org/package=rjson.
  16. Jawaid W (2023). enrichR: Provides an R Interface to 'Enrichr'. R package version 3.2, https://CRAN.R-project.org/package=enrichR.
  17. Xie Y, Cheng J, Tan X (2023). DT: A Wrapper of the JavaScript Library 'DataTables'. R package version 0.28, https://CRAN.R-project.org/package=DT.
  18. Wickham H, François R, Henry L, Müller K (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.10, https://CRAN.R-project.org/package=dplyr.
  19. R Special Interest Group on Databases (R-SIG-DB), Wickham H, Müller K (2022). DBI: R Database Interface. R package version 1.1.3, https://CRAN.R-project.org/package=DBI.
  20. Dowle M, Srinivasan A (2022). data.table: Extension of data.frame. R package version 1.14.6, https://CRAN.R-project.org/package=data.table.
  21. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2023). shiny: Web Application Framework for R. R package version 1.7.4.1, https://CRAN.R-project.org/package=shiny.
  22. Kipp A and Percival C (2023). scrypt: Key Derivation Functions for R based on Scrypt. R package version 0.1.6, https://CRAN.R-project.org/package=scrypt.
  23. Thieurmel B and Perrier V (2022). shinymanager: Authentication Management for Shiny Applications. R package version 1.0.410, https://CRAN.R-project.org/package=shinymanager.
  24. Wickham H, Averick M, Bryan J, Chang W, D'Agostino L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo, Yutani H (2019). Welcome to the tidyverse. Journal of Open Source Software. 2019; 4:43, 1686. doi: 10.21105/joss.01686.
  25. https://www.ebi.ac.uk/chembl/
  26. Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, de Veij M, Ioannidis H, Mendez Lopez D, Mosquera JF, Magarinos MP, Bosc N, Arcila R, Kizilören T, Gaulton A, Bento AP, Adasme MF, Monecke P, Landrum GA, Leach AR. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Research, Volume 52, Issue D1, 5 January 2024, Pages D1180–D1192, https://doi.org/10.1093/nar/gkad1004.
  27. Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP. ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res. 2015 Jul 1;43(W1):W612-20. doi: 10.1093/nar/gkv352. Epub 2015 Apr 16. PMID: 25883136; PMCID: PMC4489243.