User: c.bettencourt

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Posts by c.bettencourt

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Comment: C: limma Multi-level Experiments correcting for continuous covariates
... I agree with you that zero DMPs is not necessarily wrong! In our case, I think zero significant differences in comparisons like (MSA_O - MSA_C) - (CTRL_O - CRTL_C) may tell us that the effect size of the overlapping (patients and controls) significant differences between tissues (Occipital vs Cereb ...
written 14 months ago by c.bettencourt0
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Comment: C: Paired samples and interactions models with bumphunter?
... Hi guys, I am also interested in this type of analysis. Have any of you made any progress? Many thanks! ...
written 14 months ago by c.bettencourt0
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Comment: C: limma Multi-level Experiments correcting for continuous covariates
... On the (MSA_OCTX - MSA_CRBL) - (CTRL_OCTX - CTRL_CRBL) type of contrast all adj. p-values are very high and none falls below 0.05. I will have to think a bit more about point 4.  Many thanks Aaron for all the great advise and explanations! ...
written 14 months ago by c.bettencourt0
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Comment: C: limma Multi-level Experiments correcting for continuous covariates
... Dear Aaron, many thanks for your great explanation. As I am new to this type of analysis, I would just like to double check and/or clarify a few things: 1) Would you run all contrasts at once or separately by question 1, 2 and 3? 2) Regarding your question about what does "fCTRL_CRBL" means, I am ...
written 14 months ago by c.bettencourt0
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Comment: C: limma Multi-level Experiments correcting for continuous covariates
... Many thanks Aaron for getting back to me. MSA (multiple system atrophy) is the disease group (CTRL are the controls), and there are 3 brain tissues per individual (CRBL, FCTX, and OCTX), not equally affected in this disease. OCTX is unaffected, FCTX is mildly affected and CRBL is largely affected i ...
written 14 months ago by c.bettencourt0
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limma Multi-level Experiments correcting for continuous covariates
... I have DNA methylation data from Illumina EPIC arrays for 10 patients and 6 controls, with 3 different tissues per individual (run in 3 different days). I have normalised data (BMIQ, champ.norm()) and removed batch affect (champ.runCombat()) following ChAMP pipeline. Two tissues are affected in the ...
limma methylation limma design matrix covariates epic written 14 months ago by c.bettencourt0 • updated 14 months ago by Aaron Lun25k

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