Template:Novel motif: Difference between revisions
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lengthMenu: [[5,10,-1], [5,10,"All"]], | lengthMenu: [[5,10,-1], [5,10,"All"]], | ||
data: h_data, | data: h_data, | ||
columns: [ | columns: [ | ||
{ title: "FF samples", 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", 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") ; | ||
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lengthMenu: [[5,10,-1], [5,10,"All"]], | lengthMenu: [[5,10,-1], [5,10,"All"]], | ||
data: m_data, | data: m_data, | ||
columns: [ | columns: [ | ||
{ title: "FF samples", 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", 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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lengthMenu: [[5,10,-1], [5,10,"All"]], | lengthMenu: [[5,10,-1], [5,10,"All"]], | ||
order: [[ 2, "asc" ]], | order: [[ 2, "asc" ]], | ||
columns: [ | columns: [ | ||
{ | {"orderable": false}, | ||
{ | {"orderable": false}, | ||
{ | {type: "html-number", width: "80pt", 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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lengthMenu: [[5,10,-1], [5,10,"All"]], | lengthMenu: [[5,10,-1], [5,10,"All"]], | ||
order: [[ 2, "asc" ]], | order: [[ 2, "asc" ]], | ||
columns: [ | |||
{ | {"orderable": false}, | ||
{ | {"orderable": false}, | ||
{ | {type: "html-number", width: "80pt", 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") ; | ||
Revision as of 15:18, 17 January 2020
| name: | {{{name}}} |
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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 ID | GO Term | p-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 ID | GO Term | p-value |
|---|---|---|
| {{{great_results_mouse}}} |
