User: wewolski

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wewolski10
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10
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New User
Location:
Zurich
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3 months, 1 week ago
Joined:
4 years, 4 months ago
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Posts by wewolski

<prev • 11 results • page 1 of 2 • next >
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Comment: C: moderated p-values limma style from std.error, df, estimate data.
... _You need the residual variance (also known as residual standard error or residual mean square) and the residual df._ These - residual standard error, residual df - I do have for each model. Obviously, I than could then run the `squeezeVar` function as intended. But then how do I update the std:Er ...
written 8 months ago by wewolski10
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Comment: C: moderated p-values limma style from std.error, df, estimate data.
... Dear Gordon, The table is not the table of a single model on one subject but, the result of running the same model on several hundred of subjects and then computing the same contrast on all models. I updated my post to clarify this a bit more. Sorry that it wasn't more precise, I updated it accord ...
written 8 months ago by wewolski10
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moderated p-values limma style from std.error, df, estimate data.
... Hello, I do have a data frame `my_dataframe` with parameter estimates (i.e. linear combinations of the model parameters - contrasts). "ID" "Contrast" "Estimate" "Std. Error" "df" "t-value" "lower" "upper" "Pr(>|t|)" 1 "A - B" 3.2 .... ...
limma written 8 months ago by wewolski10
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Getting Significance for second factor (bioreplicate/subject) when using limma.
... Daer Forum, I want to get an estimate of the pairing variable BioReplicate What I would do when using linear model is: x1<-rnorm(10) x2<-1+rnorm(10) # Now create a dataframe for lme myDat <- data.frame(c(x1,x2), c(rep("x1", 10), rep("x2", 10)), rep(paste("S", seq(1,10), sep=""), 2)) na ...
limma anova written 18 months ago by wewolski10 • updated 18 months ago by Aaron Lun25k
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Comment: C: limma - contrast.fit returns coefficients without names
... Thank you! [ ] default behaviour strikes again! "Omitting drop = FALSE when subsetting matrices and data frames is one of the most common sources of programming errors. (It will work for your test cases, but then someone will pass in a single column data frame and it will fail in an unexpected and ...
written 24 months ago by wewolski10
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limma - contrast.fit returns coefficients without names
... I have the following code: fit <- lmFit(intmat , designMatrix) tmpfit <- fit[,-1] lmfit.cont <- contrasts.fit(fit[,-1], cont[-1,2]) lmfitebayes <- eBayes(lmfit.cont)​ topTable(lmfitebayes, coef=name, number=Inf) Which fails when executing topTable sometimes. Thats because contrast. ...
limma written 2.0 years ago by wewolski10 • updated 2.0 years ago by Aaron Lun25k
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Comment: C: Why does limma contrast.fit produces NA's for all contrast although missing data
... "Is it possible that the expression values have simply failed to meet some detection threshold for this condition?" No, it is not. Let me assure you, I did try to understand and model where the missingness is coming from.  If I used the imputation you suggest I would introduce a heavy bias. Bias is ...
written 2.3 years ago by wewolski10 • updated 2.3 years ago by Gordon Smyth39k
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Comment: A: Why does limma contrast.fit produces NA's for all contrast although missing data
... Thanks, Gordon, I have now a solution based on the post:  https://support.bioconductor.org/p/19879/ but it is a hack, workaround, and features usually do not need workarounds.  Still, if you think that contrast.fit not handling NA is a feature why not just add contrast.fit.NA to limma which is a ...
written 2.3 years ago by wewolski10 • updated 2.3 years ago by Gordon Smyth39k
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Why does limma contrast.fit produces NA's for all contrast although missing data only in one condition?
... I will try to describe briefly what I am doing: my design matrix designMatrix <- model.matrix( ~ 0 + Condition + Plant, data=grp2$annotation_) > unique(grp2$annotation_$Condition) [1] "PC_16h_sys"  "PC_16h_test" "PC_38h_sys"  "PC_38h_test" "PC_96h_sys"  "PC_96h_test" Then I define the con ...
limma contrast lmfit missing data makecontrasts written 2.3 years ago by wewolski10 • updated 2.3 years ago by Gordon Smyth39k
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limma for Paired Samples vs. lme
... Hi, Just comparing the output of limma and lme. The model that I fit with lme is : le <- lme(lmIntensity ~ Condition, random = ~1|Donor, data = x) Since I have repeated measures (Condition) on the same Donor and I am not interested in the Donor effect I model it as random effect. In lim ...
limma linear model paired samples written 2.6 years ago by wewolski10 • updated 2.6 years ago by Aaron Lun25k

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