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Template:Novel motif: Difference between revisions

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(Undo revision 4578710 by A-Kondo (talk))
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Line 1: Line 1:
<html>
{{Loading|loadingimage=sprites.gif}}<html>
<style type="text/css" title="currentStyle">
<style type="text/css" title="currentStyle">
@import "/5/sstar/rb_js/html5button/datatables.css";
@import "/5/sstar/rb_js/html5button/datatables.css";
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table.details th { width:10%; background-color: #5A5FB5; color: white;}
table.details th { width:10%; background-color: #5A5FB5; color: white;}
</style>
</style>
<script type="text/javascript" src="/5/sstar/rb_js/jquery-1.7.1.min.js"></script>
<script type="text/javascript" src="/5/sstar/rb_js/html5button/datatables.js"></script>
<script type="text/javascript" src="/5/sstar/rb_js/html5button/datatables.js"></script>
<script type="text/javascript" src="/5/sstar/rb_js/custom/convert.js"></script>
<script type="text/javascript" src="/5/sstar/rb_js/custom/convert.js"></script>
<script type="text/javascript">
</script>
</script>




<script type="text/javascript">
$(document).ready(function() {
$(document).ready(function() {


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scrollY: "300px",
scrollY: "300px",
scrollCollapse: true,
scrollCollapse: true,
pLength: 10,
pageLength: 10,
lengthMenu: [[5,10,-1], [5,10,"All"]],
lengthMenu: [[5,10,-1], [5,10,"All"]],
data: h_data,
data: h_data,
        columnDefs: [
columns: [
          { targets: 0, orderable: false},
{ "title": "FF samples", "orderable": false, render: function(data, type, row, meta) {
          { targets: 1, width: "50pt"}
return '<a href="/5/sstar/index.php/FF:'+row[ 0 ].split('.').pop()+'">'+decodeURIComponent(row[ 0 ])+'</a>';
        ],
} },
columns: [{ title: "FF samples", render: function(data, type, row, meta) {
{ title: "p-value",  type: "numeric", "width": "50pt", render: function (data, type, row, meta) {                     
return '<a href="/5/sstar/index.php/FF:'+row[ 0 ].split('.').pop()+'">'+decodeURIComponent(row[ 0 ])+'</a>';
        var num = row[1];
} },
          return exp_converter(num,2,"e");
{ title: "p-value",  type: "numeric", width: "50pt" , render: function (data, type, row, meta) {                     
} }
          var num = row[1];
],
          return exp_converter(num,2,"e") ;
} }],
order: [[ 1, "asc" ]],
order: [[ 1, "asc" ]],
});
});




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scrollY: "300px",
scrollY: "300px",
scrollCollapse: true,
scrollCollapse: true,
     pLength: 10,
     pageLength: 10,
lengthMenu: [[5,10,-1], [5,10,"All"]],
lengthMenu: [[5,10,-1], [5,10,"All"]],
data: m_data,
data: m_data,
columnDefs: [
      { targets: 0, orderable: false},
      { targets: 1, width: "50pt"}
],
columns: [
columns: [
{ title: "FF samples", orderable: false, render: function(data, type, row, meta) {
{ title: "FF samples", "orderable": false, render: function(data, type, row, meta) {
return '<a href="/5/sstar/index.php/FF:'+row[0].split('.').pop()+'">'+decodeURIComponent(row[ 0 ])+'</a>';
return '<a href="/5/sstar/index.php/FF:'+row[0].split('.').pop()+'">'+decodeURIComponent(row[ 0 ])+'</a>';
}
}
},
},
{ title: "p-value", type: "numeric", width: "50pt", render: function (data, type, row, meta) {                     
{ title: "p-value", width: "50pt", type: "numeric", render: function (data, type, row, meta) {                     
               var num =row[1];
               var num =row[1];
               return exp_converter(num,2,"e") ;
               return exp_converter(num,2,"e");
}
}
}
}
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order: [[ 1, "asc" ]],
order: [[ 1, "asc" ]],
});
});


// GREAT table for human start
// GREAT table for human start
$('#great_table_human').DataTable({
$('#great_table_human').DataTable({
dom: 'lfrtip',
dom:'lfrtip',
scrollY: "300px",
scrollY:"300px",
scrollCollapse: true,
scrollCollapse:true,
     pLength: 10,
     pageLength:10,
     lengthMenu: [[5,10,-1], [5,10,"All"]],
     lengthMenu:[[5,10,-1], [5,10,"All"]],
order: [[ 2, "asc" ]],
order:[[ 2, "asc" ]],
columnDefs: [
columns:[
{ targets: 0, orderable: false },
{"orderable":false},
{ targets: 1, orderable: false },
{"orderable":false},
{ targets: 2, type: "html-number", width: "80pt" }
{type: "html-number", width: "80pt", render: function(data, type, row, meta) {
],
               var num =row[2];
columns: [
               return exp_converter(num,2,"e");
{title: ""},
{title: ""},
{title: "FF samples", render: function(data, type, row, meta) {
               var num =row[1];
               return exp_converter(num,2,"e") ;
}
}
}
}
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// GREAT table for mouse start
// GREAT table for mouse start
$('#great_table_mouse').DataTable({
$('#great_table_mouse').DataTable({
dom: 'lfrtip',
scrollY: "300px",
scrollY: "300px",
scrollCollapse: true,
scrollCollapse: true,
     pLength: 10,
     pageLength: 10,
     lengthMenu: [[5,10,-1], [5,10,"All"]],
     lengthMenu: [[5,10,-1], [5,10,"All"]],
order: [[ 2, "asc" ]],
order: [[ 2, "asc" ]],
columnDefs: [
{ targets: 0, orderable: false },
{ targets: 1, orderable: false },
{ targets: 2, type: "html-number", width: "80pt" }
],
columns: [
columns: [
{title: ""},
{"orderable": false},
{title: ""},
{"orderable": false},
{title: "FF samples", render: function(data, type, row, meta) {
{type: "html-number", width: "80pt", render: function(data, type, row, meta) {
               var num =row[1];
               var num =row[2];
               return exp_converter(num,2,"e") ;
               return exp_converter(num,2,"e");
}
}
}
}
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}}
}}
----
----
<table id="h_pval_table"></table>
<table id="h_pval_table" class="ff_sample_list stripe cell-border order-column compact dataTable no-footer"></table>
<br><br>
<br><br>
{{Fontsize|3|Association to promoter expression in mouse samples}}
{{Fontsize|3|Association to promoter expression in mouse samples}}
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}}
}}
----
----
<!--
<table id="m_pval_table" class="ff_sample_list stripe cell-border order-column compact dataTable no-footer"></table>
<span id="export_tool"></span>
-->
<table id="m_pval_table"></table>
<br><br>
<br><br>
{{Fontsize|3|GREAT analysis results for human}}
{{Fontsize|3|GREAT analysis results for human}}
Line 178: Line 158:
}}
}}
----
----
<table id="great_table_human">
<table id="great_table_human" class="ff_sample_list stripe cell-border order-column compact dataTable no-footer">
<html><thead></html>
<html><thead></html>
<th>GO ID</th><th>GO Term</th><th>p-value</th>
<th>GO ID</th><th>GO Term</th><th>p-value</th>
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----
----
{{#if:{{{great_results_mouse}}}|
{{#if:{{{great_results_mouse}}}|
<table id="great_table_mouse">
<table id="great_table_mouse" class="ff_sample_list stripe cell-border order-column compact dataTable no-footer">
<html><thead></html>
<html><thead></html>
<th>GO ID</th><th>GO Term</th><th>p-value</th>{{#arraydefine:tmp_go_stat_mouse|{{{great_results_mouse}}}|!}}
<th>GO ID</th><th>GO Term</th><th>p-value</th>{{#arraydefine:tmp_go_stat_mouse|{{{great_results_mouse}}}|!}}

Latest revision as of 16:22, 24 September 2021

name:{{{name}}}

Association to promoter expression in human samples<b>Summary:</b>Significance of the correlation with CAGE expression, human <br><b>Analyst:</b> Michiel de Hoon<br><br>link to source dataset <br>data



Association to promoter expression in mouse samples <b>Summary:</b>Significance of the correlation with CAGE expression, mouse <br><b>Analyst:</b> Michiel de Hoon<br><br>link to source dataset <br>data



GREAT analysis results for human <b>Summary:</b>Genomic Enrichment of Annotations Tool (GREAT, Nat Biotechnol. 2010 May;28(5):495-501) is used to identify, both in human and in mouse, the gene ontology terms of biological processes enriched given the predicted TFBSs, and evaluated the overlap in the top-500 gene ontology terms between human and mouse. For each novel motif, the P value for the overlap was then evaluated by calculating its relative rank with respect to this background distribution.<br>Analyst: Michiel de Hoon<br><br>link to source dataset <br>data



GREAT analysis results for mouseGenomic Enrichment of Annotations Tool (GREAT, Nat Biotechnol. 2010 May;28(5):495-501) is used to identify, both in human and in mouse, the gene ontology terms of biological processes enriched given the predicted TFBSs, and evaluated the overlap in the top-500 gene ontology terms between human and mouse. For each novel motif, the P value for the overlap was then evaluated by calculating its relative rank with respect to this background distribution.<br>Analyst: Michiel de Hoon <br><br>link to source dataset <br>data

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