## User: Mr.RB

Mr.RB10
Reputation:
10
Status:
New User
Location:
Last seen:
2 months, 4 weeks ago
Joined:
5 months ago
Email:
r*******@outlook.com

#### Posts by Mr.RB

<prev • 16 results • page 1 of 2 • next >
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... Aaah I see. Although I don't get the statistics after the dispersion estimates (currently looking at the paper) I could imagine that the model would be like y = b0 + condition2 *b1, where b0 = mean(condition1) and b1 indicates the increase in mean for condition2.  And then test with H0: b1 == 0. How ...
written 12 weeks ago by Mr.RB10
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... When calculating the dispersion DESeq2 will only give one dispersion value for each gene, as can also be seen in the dispersion plot. I don't have much knowledge about statistics, but as far as I know we basically want to compare a distribution from condition 1 vs condition 2 and see whether there i ...
written 12 weeks ago by Mr.RB10 • updated 12 weeks ago by Ryan C. Thompson6.8k
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... That's why i said " the model would look like this I suppose") , but apparently I was wrong here, but then the plot I mentioned (coefficient 1 vs coefficient) does makes sense. Anyway I will re-read it ...
written 12 weeks ago by Mr.RB10
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... hmm I just checked it: edgeR.fit <- edgeR::glmFit(edgeR.dgelist , design) edgeR.glrt <- edgeR::glmLRT(edgeR.fit, contrast = mc) head(edgeR.fit$coefficients,2) # groupcon grouptreat #gene1 -7.057066 -7.104031 #gene2 -6.580015 -6.706760 head(edgeR.glrt$table,2) # logFC logCP ...
written 12 weeks ago by Mr.RB10
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... Aaah that's clear! Got all the steps now. Thankyou for the great and quick support for the questions! ...
written 12 weeks ago by Mr.RB10
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... Sorry for bothering you again! But I can't figure what these coefficients mean. Let's say we compare two treatments, this will result in two coefficients: the first one is the baseline for group1 and the second the 2 vs 1 comparison (according to the user guide). Indeed when I plot coefficient 1 aga ...
written 12 weeks ago by Mr.RB10
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... Sorry if I wasn't clear I actually ment whether I could produce a plot like the last figure on this page. Let's say I done my tag wise dispersion estimates based on the whole data, then select a single gene and its counts and dispersion. Like so: dat <- read.table(text ="con.1 con.2 treat.1 tre ...
written 3 months ago by Mr.RB10
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... Do you have any suggestions to show what a GLMfit actually does, or to explain this simply. I think the testing of coefficients would be easy understandable, but I completely forgot the fitting part... I know about the gof() plot but I think this will more focus on the results than what it actually ...
written 3 months ago by Mr.RB10
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... Thankyou! the prior.df=0 to prior.df = x should really visually clarify the compromis! ...
written 3 months ago by Mr.RB10
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... Little background I have used edgeR quite some time now and try to teach others to use it as well. Most of them are familiar with hypothesis testing, means, variances etc, but none looked at RNA-seq data before. So the testing part, i.e. comparing the distribution of two treatments is understandabl ...
written 3 months ago by Mr.RB10 • updated 3 months ago by Aaron Lun21k

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