{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% import "bootstrap/fixes.html" as fixes %} {% import "bootstrap/utils.html" as util %} {% block title %}Enriched Pathways{% endblock %}xz {% block styles %} {{ super() }} {% endblock %} {% block scripts %} {{ super() }} {% include "dependencies/datatables.html" %} {% endblock %} {% block content %}

First, select your pathways of interest and then, choose the type of analysis to perform. The "Overlap View" displays the boundaries between the selected pathways represented as Venn or Euler diagrams. The "Cluster View" renders an interactive dendrogram of the pathways clustered based on their distances. Finally, the "Network View" displays the knowledge around the selected pathways as well as the similarity between them enabling to identify interplays.


{% for resource_name, pathway_dict in query_results.items() %}

{{ resource_name|upper }}

{% for pathway_id, enriched_pathway in pathway_dict.items() %} {% endfor %}
Pathway Name Resource Identifier Adjusted p-value Genes Mapped Pathway Size
{{ enriched_pathway["pathway_name"] }} {% if resource_name == "reactome" %} {{ enriched_pathway["pathway_id"] }} {% elif resource_name == "kegg" %} {{ enriched_pathway["pathway_id"] }} {% elif resource_name == "wikipathways" %} {{ enriched_pathway["pathway_id"] }} {% elif resource_name == "msig" %} {{ enriched_pathway["pathway_id"] }} {% else %} {{ enriched_pathway["pathway_id"] }} {% endif %} {{ enriched_pathway["q_value"] }} {{ enriched_pathway["mapped_proteins"] }} {{ enriched_pathway["pathway_size"] }}
{% endfor %}
{% include "footer.html" %} {% endblock %}