User: mikhael.manurung

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Posts by mikhael.manurung

<prev • 38 results • page 1 of 4 • next >
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Comment: C: Adjusting for known covariates before coexpression analysis with WGCNA
... Dear Peter, Thank you for your prompt response. For `empiricalBayesLM`, would you advise feeding the group variables into the `retainedCovariates` argument? Best, Mikhael ...
written 1 day ago by mikhael.manurung50
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Adjusting for known covariates before coexpression analysis with WGCNA
... Dear all, I would like to adjust my whole-blood RNA-Seq count data matrix for cell type composition (obtained from hematological analysis & flow cytometry) before doing a coexpression network analysis with `WGCNA`. So far, I did the following: ``` # I use DESeq2's vst to remove mean-variance ...
sva wgcna coexpression written 1 day ago by mikhael.manurung50 • updated 1 day ago by Peter Langfelder1.9k
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Comment: C: Differential gene expression using edgeR and using a generalised linear model (
... Did your supervisor elaborate more on the lack of need to normalise your data? Somehow I suspect that your supervisor actually meant that you do not need to transform your count data to something else such as with the `voom` transformation from `limma`. Anyway, `DGEList` object can be made as such: ...
written 1 day ago by mikhael.manurung50
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Answer: A: Gene expression associated with continuous (quantitative) traits (Limma/edgeR/co
... I do not think you can use a continuous variable in your design matrix for either ```limma``` or ```edgeR```. Well, you can use it as a covariate for adjustment of confounding or batch effects. It is possible to correlate the expression of your genes with your continuous predictor, provided you ha ...
written 9 days ago by mikhael.manurung50
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Comment: C: Plot genes with similar expression patterns
... I think the ```maSigPro``` package would be the one that you are looking for. I hope this helps. ...
written 9 days ago by mikhael.manurung50
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Comment: C: edgeR and batch effect correction for differential analysis
... ```removeBatchEffect``` should not be used for DE analysis but for other downstream analysis, such as PCA. For adjustment of DE analysis, indeed you should include ```columndata$days``` in design matrix. Therefore, your ```dge``` object should be used. This is important because the linear modeling s ...
written 9 days ago by mikhael.manurung50
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Comment: C: limma random effects and multi level experiments
... Hi David, To me, it seems like you were using sample ID (i.e. column names) instead of the donor ID for the blocking variable in the ```duplicateCorrelation```. Perhaps I am wrong. Other than that, I do not see any problem with your code. ...
written 5 weeks ago by mikhael.manurung50
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Comment: C: How to regress phenotype ~ gene + covars in limma?
... Could you elaborate what you are trying to achieve? Are you looking for the most important genes that may affect the phenotype? Perhaps you would be better off doing Lasso regression for that purpose. ...
written 5 weeks ago by mikhael.manurung50
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Comment: C: LIMMA how to model unidentified source of covariate
... Hi Scheran, perhaps you can make a biplot to see the genes that drive the variance in your PCA analysis. See this [link][1] for a description of the biplot. I hope this helps. And one more thing, perhaps there is also a component of technical (e.g. batch effect) instead of biological variation in yo ...
written 6 weeks ago by mikhael.manurung50
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Comment: C: Flow cytometry optimization of transformation parameters
... Or you can merge all of your fcs files to get a better estimate of the transformation parameters ```r fs_merge <- flowFrame(fsApply(fset, exprs)) trans <- estimateLogicle(fs_merge, chnls) # apply transformation to your flowSet fset <- trans %on% fset ``` ...
written 10 weeks ago by mikhael.manurung50

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