WGCNA modules based on primary human hepatocytes (TG-GATEs)

LACDR - Leiden University - v 1.0









































                  




















Select the conditions to visualize the correlation on the plot. To plot all the conditions, select Yes for BOTH modules.
Module 1:
Module 2:

Welcome to the upload tab!

Here you can upload your own set of data and visualize the scoring of the modules built with TG-GATEs data (Primary Human Hepatocytes).
For more details, navigate in the other tabs: in the menus you will find now also your experiment(s). Please note that your data will remain loaded until you press the Reset Selection button.
Visit the help section for more details





General overview





Modules overview


Genes overview



Genes excluded from the uploaded datasets:

Welcome to the help page!

The app 'WGCNA modules based on primary human hepatocytes (TG-GATEs)' is displaying the co-expression network analysis (WGCNA) applied to the public available transcriptomic dataset TG-GATEs.

Upload

In the upload tab it is possible to calculate new EGS on the modules using a new set of data uploaded by the user. See Sutherland et al., 2016 for details about the calculation of new EGS.

New data can be uploaded via two ways:

  1. by uploaded a .txt file

  2. by pasting an identifier provided by the IBRI tool (http://ctox.indianabiosciences.org/), where preliminar analysis are performed.

In the scenario number 1, the user is REQUIRED to upload a .txt file with the following structure:

 experiment_id gene_symbol    log2_fc      p_value       p_adj
             1         A2M -0.1924495 4.975817e-05 0.002895032
             1        AARS  0.3926530 5.260567e-03 0.031292362
             1    AASDHPPT -0.2320112 4.377097e-01 0.479904688
             1        AATF -0.5014265 6.046809e-02 0.122053491
             1       ABCA1 -0.2274920 2.228942e-01 0.286171794

The experiment_id variable is a number that identifies the experiment uploaded, e.g. treatment with Tunicamycin, 10 uM, for 24h. Different numbers indicate a different experiment. Then in this table, different experiments are one after the other.

For every experiment uploaded, you will include as many rows as the genes measured. Genes are identified by the HGNC gene names in the gene_symbol variable. Different experiments may have different genes measured/included.

For every gene, log2 of the fold changes is included in column log2_fc, together with p_value and a p-value adjusted for multiple comparaisons (p_adj).

Variables names are case sensitive (please use exactly the same header).

Upload a .txt file, tab-separated


When the file is correctly uploaded the page is refreshed:

  • on the top of the page you have a notification of the file uploaded

  • a general overview plot is generated, where you can compare new EGS with TG-GATEs EGS

  • a module overview table is generated (it is possible to download it)

  • two tables in the genes overview section are generated. The first table warns about the rate of the exclusions of genes. They could be excluded because not in the TG-GATEs datasets or because excluded in the module built during WGCNA. The second table provides, for every gene, the assignment to the module and the coverage of the module. Some modules could have a percentage of coverage less than 100 because some genes that were present on the TG-GATEs dataset were not uploaded in the new table (it is possible to download it).

Additionally, it is possible to visit the other tabs, now refreshed for the presence of the new experiments (you can find the new experiments in the dropdown lists, in the tables and in the plots).

To delete your uploaded data and abandon the session, press the Reset selection button. The plots and tables will disappear and the notification on the top of the page as well. In the other tab you will not be able to find anymore your new data.

Credits and contacts

(alphabetical order)

Callegaro Giulia1 (g.callegaro@lacdr.leidenuniv.nl), den Hollander Wouter1, Kunnen Steven J.1 (s.j.kunnen@lacdr.leidenuniv.nl), Mollon Jennifer2, Stevens James L.1,3, van de Water Bob1, Webster Yue3

1 Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

2 AbbVie Deutschland GmbH & Co KG, Knollstra├če 67061, Ludwigshafen, Germany

3 Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, United States of America