{% 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 %} {% import "meta/macros.html" as compath %} {% block content %}
Gene Symbols Submitted ({{ submitted_gene_set|length }}) | {% for hgnc_symbol in submitted_gene_set %} {{ hgnc_symbol }} {{ "," if not loop.last }} {% endfor %} |
Genes not in any pathway ({{ genes_not_in_pathways|length }}) | {% for hgnc_symbol in genes_not_in_pathways %} {{ hgnc_symbol }} {{ "," if not loop.last }} {% endfor %} |
Number of Pathways Mapped | {{ number_of_pathways }} |
Select All Pathways |
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.
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"] }} |