User: mikhael.manurung

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Posts by mikhael.manurung

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Comment: C: Controlling numerical covariates: differential gene expression with limma voom
... Hi, I would really appreciate it if you upvote my answer if you find it helpful :) ...
written 5 weeks ago by mikhael.manurung10
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Comment: C: Count filter for differential expression
... The results fit better to what we see in the lab with applying a count filter, but do not differ too much between (1) and (2). What did you mean by "the results fit better"? Did you arbitrarily choose your cutoff based on the number of differentially expressed genes? If that is the case then I think ...
written 5 weeks ago by mikhael.manurung10
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Comment: C: RNA seq analysis
... Hi Marak, fellow newbie here. I think differential expression workflow is enough if you only want to find cancer-related genes by comparing cancer against normal tissues (just an example). Simple methods are fine too, you know. Before you're diving in too deep with all those fancy machine learning ...
written 6 weeks ago by mikhael.manurung10
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Answer: A: Controlling numerical covariates: differential gene expression with limma voom
... Hi Onyi, Your design and contrast matrices are correct. I am not sure about your variable naming convention, but please make sure the TMM input variable to your voom command is the expression set. Last but not least, it is now recommended to do the duplicateCorrelation calculation twice each. Also k ...
written 6 weeks ago by mikhael.manurung10
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Comment: C: nestedF in decideTests: an analogue of Anova with post-hoc t-tests?
... I did not elaborate on that for the sake of brevity, but then apparently clarity was sacrificed. I fit an lmFit object named fit with the contrasts as below. fit.c <- contrasts.fit(fit, contrasts= contrast) fit2 <- eBayes(fit.c, robust=TRUE) To get the summary of DE genes in all contrasts, I ...
written 3 months ago by mikhael.manurung10
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Comment: C: nestedF in decideTests: an analogue of Anova with post-hoc t-tests?
... Thank you for your elaborate reply, Aaron. The point that I wanted to make (or confirm) for the first question is that topTable will control for FDR by treating the p-values as a single vector of values, just like supplying method=separate in decideTests to adjust for FDR separately for each tested ...
written 3 months ago by mikhael.manurung10
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Comment: C: nestedF in decideTests: an analogue of Anova with post-hoc t-tests?
... Dear Prof. Smyth, Suppose that I want to test the following null hypotheses: there are no differences between time points (t0, t1, t2) in group A, and there are no significant differences in pairwise comparisons of time points (see the contrast as defined below). I find single-factor design easier ...
written 3 months ago by mikhael.manurung10
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Comment: C: extracting the genes associated with the groups or samples in pheatmap.
... Hello Morteza, You said in your question that you have determined the genes that are significant. Which package did you use to determine that? If you use limma, then its user guide will surely benefit you. However, I get the feeling that what you have got so far is only the expression matrix. ...
written 3 months ago by mikhael.manurung10
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Comment: C: Design and Contrast Matrix for Linear Model. To Combine or not to Combine?
... Speaking of F-test, I actually asked another question in this forum: question in this forum. Briefly, would the above command yield the same result as using decideTests with "method = nestedF" argument? ...
written 3 months ago by mikhael.manurung10
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nestedF in decideTests: an analogue of Anova with post-hoc t-tests?
... Dear all, I would like to extend the discussion on my previous question regarding the contrast matrix. Briefly, I have data from two groups that are followed longitudinally at three different time points. After going through the workflow of the limma package, (lmFit, contrast fit, and eBayes), now ...
limma anova decidetests written 3 months ago by mikhael.manurung10 • updated 3 months ago by Gordon Smyth36k

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