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Line 30: Line 30:
}
}


     $('#h_pval_table').dataTable({
     $('#h_pval_table').DataTable({
"sScrollY": "300px",
dom: 'lfrtip',
"bScrollCollapse": true,
scrollY: "300px",
"bPaginate": true,
scrollCollapse: true,
"bFilter": true,
pLength: 10,
"bInfo": true,
lengthMenu: [[5,10,-1], [5,10,"All"]],
                "iDisplayLength": 10,
data: h_data,
                "aLengthMenu": [[5,10,-1], [5,10,"All"]],
        columnDefs: [
"aaData": h_data,
          { targets: 0, orderable: false},
"aoColumns": [{ "sTitle": "FF samples", "bSortable": false, "fnRender": function(obj) {
          { targets: 1, width: "50pt"}
return sReturn = '<a href="/resource_browser/index.php/FF:'+obj.aData[ obj.iDataColumn ].split('.').pop()+'">'+decodeURIComponent(obj.aData[ obj.iDataColumn ])+'</a>';
        ],
} },{ "sTitle": "p-value",  "sType": "numeric", "sWidth": "50pt" , "aTargets": [1],"fnRender": function (obj) {                     
columns: [{ title: "FF samples", render: function(data, type, row, meta) {
                  var num =obj.aData[obj.iDataColumn];
return '<a href="/resource_browser/index.php/FF:'+obj.aData[ obj.iDataColumn ].split('.').pop()+'">'+decodeURIComponent(obj.aData[ obj.iDataColumn ])+'</a>';
                  return exp_converter(num,2,"e") ;
} },
} }],
{ title: "p-value",  type: "numeric", width: "50pt" , render: function (data, type, row, meta) {                     
"aaSorting": [[ 1, "asc" ]],
          var num = row[1];
});
          return exp_converter(num,2,"e") ;
} }],
order: [[ 1, "asc" ]],
});




Line 58: Line 61:
}
}


    $('#m_pval_table').DataTable({
$('#m_pval_table').DataTable({
dom: 'lfrtip',
dom: 'lfrtip',
scrollY: "300px",
scrollY: "300px",
scrollCollapse: true,
scrollCollapse: true,
                pLength: 10,
    pLength: 10,
lengthMenu: [[5,10,-1], [5,10,"All"]],
lengthMenu: [[5,10,-1], [5,10,"All"]],
data: m_data,
data: m_data,
columnDefs: [
columnDefs: [
          { targets: 0, orderable: false},
      { targets: 0, orderable: false},
          { targets: 1, width: "50pt"}
      { targets: 1, width: "50pt"}
],
],
columns: [{ title: "FF samples",  orderable: false, render: function(data, type, row, meta) {
columns: [
return '<a href="/resource_browser/index.php/FF:'+row[0].split('.').pop()+'">'+decodeURIComponent(row[ 0 ])+'</a>';
{ title: "FF samples",  orderable: false, render: function(data, type, row, meta) {
} },
return '<a href="/resource_browser/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",  type: "numeric", width: "50pt", 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") ;
}}],
}
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,
    pLength: 10,
        lengthMenu: [[5,10,-1], [5,10,"All"]],
    lengthMenu: [[5,10,-1], [5,10,"All"]],
order: [[ 2, "asc" ]],
order: [[ 2, "asc" ]],
columnDefs: [
columnDefs: [
{ targets: 0, orderable: false },
{ targets: 0, orderable: false },
{ targets: 1, orderable: false },
{ targets: 1, orderable: false },
{ targets: 2, type: "html-number", width: "80pt" }
{ targets: 2, type: "html-number", width: "80pt" }
],
],
columns: [
columns: [
{title: :""},
{title: :""},
{title: :""},
{title: :""},
{title: "FF samples", render: function(data, type, row, meta) {
{title: "FF samples", 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") ;
}}]
}
});
}
]
});
// GREAT table for human end
// GREAT table for human end


// GREAT table for mouse start
// GREAT table for mouse start
$('#great_table_mouse').DataTable({
$('#great_table_mouse').DataTable({
scrollY: "300px",
scrollY: "300px",
scrollCollapse: true,
scrollCollapse: true,
        pLength: 10,
    pLength: 10,
        lengthMenu: [[5,10,-1], [5,10,"All"]],
    lengthMenu: [[5,10,-1], [5,10,"All"]],
order: [[ 2, "asc" ]],
order: [[ 2, "asc" ]],
columnDefs: [
columnDefs: [
{ targets: 0, orderable: false },
{ targets: 0, orderable: false },
{ targets: 1, orderable: false },
{ targets: 1, orderable: false },
{ targets: 2, type: "html-number", width: "80pt" }
{ targets: 2, type: "html-number", width: "80pt" }
columns: [
columns: [
{title: :""},
{title: :""},
{title: :""},
{title: :""},
{title: "FF samples", render: function(data, type, row, meta) {
{title: "FF samples", 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") ;
}}]
}
});
}
]
});
});
// GREAT table for mouse end
// GREAT table for mouse end

Revision as of 13:34, 17 January 2020

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

GO IDGO Termp-value
{{{great_results_human}}}


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

GO IDGO Termp-value
{{{great_results_mouse}}}
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