Timeline for Validity of replacing q-value with p-values from multiple testing of different tools
Current License: CC BY-SA 3.0
3 events
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| Jul 5, 2013 at 9:15 | comment | added | JohnRos | Assuming all software provide type $I$ error control over the 20,000 genes, but in very different manners, so that their global p-value are uncorrelated (under the global null), you will have fast type $I$ error inflation. I believe however, this is implausible as each field has a small number of favorite error control methods. You could use simulated data to quantify the agreement over different software under the null. | |
| Jul 5, 2013 at 9:11 | comment | added | Arun | The problem is that the reason they go for different softwares is that there's normally a huge non-overlap between these softwares (meaning the correlation will be typically low). This is also my experience with these softwares on my data. But I choose one and fetch results conservatively. I am beginning to get the feeling that, in those cases, from what you and January point out, it'll most definitely fail...? | |
| Jul 5, 2013 at 9:07 | history | answered | JohnRos | CC BY-SA 3.0 |