From ea46f42ee640928b92947bfb204c41a482d80937 Mon Sep 17 00:00:00 2001 From: root Date: Tue, 8 May 2012 18:39:56 -0500 Subject: Add all the source codes into the github. --- web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm | 5 +++++ 1 file changed, 5 insertions(+) create mode 100755 web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm (limited to 'web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm') diff --git a/web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm b/web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm new file mode 100755 index 00000000..5003f4a6 --- /dev/null +++ b/web/tutorial/ppt/WebQTLDemo_files/slide0008_notes_pane.htm @@ -0,0 +1,5 @@ +
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  of tests (12422 tests).

What does this imply?

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.
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