## User: mikhael.manurung

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#### Posts by mikhael.manurung

<prev • 38 results • page 1 of 4 • next >
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... 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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... 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 ...
written 1 day ago by mikhael.manurung50 • updated 1 day ago by Peter Langfelder1.9k
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... 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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... 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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... 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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... 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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... 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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... 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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... 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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... 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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