{% extends "base.html" %} {% import "bootstrap/wtf.html" as wtf %} {% import "bootstrap/fixes.html" as fixes %} {% import "bootstrap/utils.html" as util %} {% block title %}{{ compath.stylize_plugin_name(STYLED_NAMES, resource) }} Pathway Database Distributions{% endblock %} {% block styles %} {{ super() }} {% endblock %} {% block scripts %} {{ super() }} {% include "dependencies/common.html" %} {% include "dependencies/datatables.html" %} {% endblock %} {% import "meta/macros.html" as compath %} {% block content %}
Explore the distribution of the pathway sizes across different resources. The aim of this plot is just to show the distribution and the outliers, in order to inspect and find the name of the outliers, please use the table in the section below.
Explore the histogram in detail using the table below.
Pathway Name | Gene Set Size |
---|---|
{% if resource == "reactome" %} {{ name }} {% elif resource == "kegg" %} {{ name }} {% elif resource == "wikipathways" %} {{ name }} {% elif resource == "msig" %} {{ name }} {% else %} {{ name }} {% endif %} | {{ size }} |
Explore the distribution of the all the genes in databases across pathways. The aim of this plot is just to show the more promiscuous genes in pathway databases, in order to inspect and find the name of the outliers, please use the table in the section below. Based on the distributions of KEGG, Reactome, and WikiPathways, we decided to show only the distribution of genes functionally annotated from zero to fifty pathways (x axis ranges from [0,50]). The reason for this is that there are a few genes acting in 100 pathways or more and disrupt that distort the plot. In order to pinpoint the most promiscuous genes, please use the sorting functionality of the table below.
Explore the histogram in detail using the table below.
Gene Symbol | Number of Pathways |
---|---|
{{ gene }} | {{ pathways_associated }} |