User: mikhael.manurung

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Posts by mikhael.manurung

<prev • 42 results • page 1 of 5 • next >
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Answer: A: Save ExpressionSet to csv file with columns and rows headers
... You can transpose the data frame and then save it, like this: ``` tmp <- t(tmp) #transpose data frame write.csv(tmp, "tmp.csv", row.names = TRUE, col.names = TRUE) # make sure to set row.names to TRUE! ``` ...
written 6 weeks ago by mikhael.manurung90
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Comment: C: Using SVA (surrogate variable analysis) to correct for 'completely' confounded c
... Hi Papyrus, I also encountered this problem but fortunately, in my dataset, the cell type compositions were not associated with my covariate of interest. If you have the cell type compositions data, why wouldn't you put those together in your formula (meth ~ age.group + cellA + cell B + cell C + a ...
written 6 weeks ago by mikhael.manurung90
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Comment: C: Differential gene expression using edgeR and using a generalised linear model (
... If it is a csv file then you can use `read.csv`. Good luck! ...
written 6 weeks ago by mikhael.manurung90
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Comment: C: Differential gene expression using edgeR and using a generalised linear model (
... Note that `list.files` will only, well, list all the files that you have within your working directory. It **DOES NOT** import the data into R. That is why the first thing that you should do is to properly import those data into R. For your data, you can easily use `read.table`. By the way, it seem ...
written 7 weeks ago by mikhael.manurung90
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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 7 weeks ago by mikhael.manurung90
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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 7 weeks ago by mikhael.manurung90 • updated 7 weeks ago by Peter Langfelder2.1k
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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 7 weeks ago by mikhael.manurung90
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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 8 weeks ago by mikhael.manurung90
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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 8 weeks ago by mikhael.manurung90
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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 8 weeks ago by mikhael.manurung90

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