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 8 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 8 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 8 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 8 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 8 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 8 months ago by c.bettencourt0 • updated 8 months ago by Aaron Lun23k

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