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authorLei Yan2015-05-12 20:47:50 +0000
committerLei Yan2015-05-12 20:47:50 +0000
commitcb5684aa004d59585167804450f64ab74d3a3412 (patch)
tree31aedac7d1fd945c893f07e2e419b4361f00925b /web/tutorial/ppt/WebQTLDemo_files/slide0008.htm
parentd11b71790f5f7c7eb9fe41704e1df7d8447371c9 (diff)
parentf228118ac112f3ebab5faf41ac9e1bc2a6e2c0fa (diff)
downloadgenenetwork2-cb5684aa004d59585167804450f64ab74d3a3412.tar.gz
Merge https://github.com/genenetwork/genenetwork2
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-<html>

<head>
<meta http-equiv=Content-Type content="text/html; charset=macintosh">
<meta name=ProgId content=PowerPoint.Slide>
<meta name=Generator content="Microsoft Macintosh PowerPoint 10">
<link id=Main-File rel=Main-File href="WebQTLDemo.htm">
<title>PowerPoint Presentation  -  Complex trait analysis, develop-ment, and
genomics</title>
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href=WebQTLDemo.ppt>
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<meta name=Description content=Jun-19-03>
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<script language=JavaScript src=script.js></script><script language=JavaScript><!--

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width:755px;height:566px;font-size:16px;background-color:#484848;clip:rect(0%, 101%, 101%, 0%);
visibility:hidden'><img src="master03_background.gif" v:shapes="_x0000_s1026"
style='position:absolute;top:0%;left:0%;width:100.0%;height:100.0%'>

<div style='position:absolute;top:1.41%;left:3.31%;width:93.77%;height:14.66%;
filter:DropShadow(Color=#000000, OffX=1, OffY=1)'>

<div class=B style='mso-line-spacing:" 0 ";mso-margin-left-alt:348;mso-text-indent-alt:
56;position:absolute;top:0%;left:2.11%;width:96.46%;height:50.6%'><span
style='mso-special-format:nobullet;display:none;color:#FBFDB8;font-family:"Monotype Sorts";
font-size:99%'>l</span><span style='font-size:106%;color:#E9EB5D;mso-color-index:
3'>The </span><span style='font-size:106%;color:#E9EB5D;mso-color-index:3'><i>App</i></span><span
style='font-size:106%;color:#E9EB5D;mso-color-index:3'> transcript neighborhood</span></div>

</div>

<img border=0 src="slide0008_image014.gif" style='position:absolute;top:10.77%;
left:5.82%;width:66.62%;height:88.33%'>

<div class=O style='position:absolute;top:30.74%;left:75.23%;width:26.22%;
height:18.72%'><span style='position:absolute;top:0%;left:0%;width:91.91%'><span
style='font-size:150%;color:#E9EB5D;mso-color-index:3'><b>Question: </b></span><span
style='font-size:133%;color:#E9EB5D;mso-color-index:3'><b>How </b></span></span><span
style='position:absolute;top:21.69%;left:0%;width:97.47%'><span
style='font-size:133%;color:#E9EB5D;mso-color-index:3'><b>many transcripts </b></span></span><span
style='position:absolute;top:40.56%;left:0%;width:100.0%'><span
style='font-size:133%;color:#E9EB5D;mso-color-index:3'><b>have correlations </b></span></span><span
style='position:absolute;top:59.43%;left:0%;width:97.97%'><span
style='font-size:133%;color:#E9EB5D;mso-color-index:3'><b>&gt;0.7? What does </b></span></span><span
style='position:absolute;top:79.24%;left:0%;width:85.35%'><span
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  <td align=left colspan=1><font face=Helvetica size=3>The answer is a strong
  Yes. A very large number of transcripts have correlations above 0.7 (absolute
  value) with App mRNA. The precise number today is 208. But this will change
  as we add more strains and arrays. In any case, this is a fairly large number
  and all of these correlations are significant at alpha .05 even when
  correcting for the enormous numbers<span style="mso-spacerun: yes">&nbsp;
  </span>of tests (12422 tests).</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Helvetica size=3>What does this imply?</font><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><br>
  </td>
 </tr>
 <tr>
  <td colspan=1></td>
  <td align=left colspan=1><font face=Helvetica size=3>That there can be
  massive codependence of expression variance among transcripts. App is NOT an
  isolated instance. This is improtant biologically and statistically. From a
  statistical perspective, we would like to know how many ÒindependentÓ test we
  effectively are performing when we use array data in this way. Are we testing
  12000 independent transcripts or just 1200 transcriptional ÒmodulesÓ each
  with blurred boarders but each with about 10 effective members. There is no
  answer yet, but we probaby have a large enough data set to begin to answer
  this question.</font><br>
  </td>
 </tr>
</table>

</div>

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