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Thread | Thread Starter | Forum | Replies | Last Post |
Optimal read length and number for plant virus discovery | HFSG | Illumina/Solexa | 0 | 08-21-2015 09:39 AM |
Design for normalization using DESeq | Ayaka | Bioinformatics | 0 | 03-07-2015 04:21 PM |
DESeq, experimental design | lmolokin | Bioinformatics | 14 | 06-12-2013 06:36 AM |
DESeq Design Issue | jpollack | Bioinformatics | 0 | 02-04-2013 11:44 AM |
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#1 |
Junior Member
Location: Canada Join Date: Oct 2016
Posts: 3
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For the 1st question, I want to know if I should run a single factor or double factor. These samples are match normal/tumor from different patients so something like this
p1N p1T p2N p2T p3N p3T do i have to run a double factor run like this: ------------condition---------patientNumber ------------<factor>--------- <factor> p1N--------normal------------ 1 p1T----------tumor ------------1 p2N---------normal-------------2 p2T----------tumor--------------2 p3N--------normal-------------3 p3T---------tumor--------------3 or a single factor like this: -------------Condition -------------<factor> p1N--------normal p1T---------tumor p2N--------normal p2T---------tumor p3N--------normal p3T---------tumor I suppose the question boils down to whether or not DESeq cares if samples come from different patients. For the 2nd question, I have a thousand match normal/tumor from the same type of tissue but from different patients, should I run DESeq on all one thousand of them or is it fine to do a smaller amount like one hundred? Last edited by Lugalbanda; 10-05-2016 at 01:53 PM. Reason: formatting |
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#2 |
Devon Ryan
Location: Freiburg, Germany Join Date: Jul 2011
Posts: 3,478
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If you have tumour/normal pairs, then you should include the pairing information
Code:
design = ~patientNumber + condition |
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#3 |
Senior Member
Location: Geneva Join Date: Feb 2012
Posts: 175
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Hi dpryan,
how slow for 1000 samples? |
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#4 |
Devon Ryan
Location: Freiburg, Germany Join Date: Jul 2011
Posts: 3,478
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@SylvainL: Good question and I don't know the answer to that. Perhaps Mike Love will pop by the thread and give a guesstimate.
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#5 |
Senior Member
Location: California Join Date: Jul 2014
Posts: 197
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It seems from the cancer literature that most/all studies using a paired tumor-normal design only have one sample from each (e.g. p1T, p1N) and not in replicate (e.g. p1Ta, p1Tb, p1Tc, p1Na, p1Nb, p1Nc). Is that appropriate, or would it be better to do things in triplicate? Say by splitting a tumor sample into 3 sections?
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#6 |
Senior Member
Location: Geneva Join Date: Feb 2012
Posts: 175
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Your replicates are paired (p1T, p1N, p2T, p2N, etc...) so there is no need to split.
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