4
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Identify differentially covered genes only between two samples
Not really an answer but an extended comment... and most likely something you don't like to hear
I guess by technical replicates, it means taking the same biological sample and making 2 methylation ...
- 1,668
3
votes
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Help with Limma-model
It looks good to me. One think I could suggest is to see the distribution of your data/model (with limma/voom).
After you get the list of your genes with:
...
- 214
3
votes
TPM or rlog(CPM) for comparing expression?
For comparing the counts of different samples from DESeq2, Michael Love recommends using the variance-stabilized transform. It'd be great if you could provide some specific code examples in your ...
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3
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Smallest group size for differential expression in limma (bulk RNA-Seq)
While I share Llopis' concern about estimating variance from 2 samples, the statement you quoted is about avoiding false positives from genes that are only expressed in a few samples. It's fairly ...
- 1,806
3
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Smallest group size for differential expression in limma (bulk RNA-Seq)
The problem with most of the methods is that use the gene's variance for each group, which can't be calculated (reliable) when the sample is <= 2. Also, statistically, it would have extremely low ...
- 4,662
3
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How to get log2 fold change of RNA-Seq data for time series experiment?
If you just want the change with time then use time as a continuous variable. The log2FC will then be the change per hour (you can use the default Wald test to ...
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2
votes
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Significant gene set testing - limma
The mroast function has an argument to specify which contrast do you want to test, quoting from the help page:
contrast
contrast for which the test is required....
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2
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Can I use a regular liner regression model when I'm working with DNA methylation data?
You can use either, but lmFit has the benefit of returning an object that can be used with eBayes() so you can pool information ...
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2
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TPM or rlog(CPM) for comparing expression?
To add to what @gringer, when you do use TPM, the normalization done is for both library size and gene length. When you use rlog, the normalization is done via median normalization (https://www.ncbi....
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Any way to filter out highly correlated genes with limma linear model?
The problem is that you are transposing the vector of GA values with t(ano$GA). Why would you do that? It produces a row matrix that is inappropriate for input to <...
- 281
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Any way to quantify the variation of genes that expressed in Affymetrix expression data?
You can use the following code to calculate the coefficient of variation:
...
- 4,662
2
votes
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R limma alternatives in Python
August 2021
To date, it seems that the response to this question is "No". However, there is a GitHub repository called edgePy, aiming to "become an ...
- 151
2
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To find genes that don't change in RNA-seq, Deseq2 has altHypothesis="lessAbs". Is there a way to make limma do the same thing for proteomics?
There is no direct support for this by best knowledge. Similar questions have been asked previously in the Bioconductor support forum, for example:
https://support.bioconductor.org/p/64300/#64793
They ...
- 551
2
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How to use the contrast function in ebayes for making different comparisons
A design matrix is a matrix of values of the grouping variable.
ANOVA needs such a matrix to know which samples belong to which group. Since limma performs an ANOVA ...
- 193
1
vote
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Assumptions of batch effect removal
You're basically subtracting a constant per-gene per-level. The relevant portion of the code is:
...
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1
vote
Identify differentially covered genes only between two samples
You may run edgeR for methylation analysis without replicates (https://f1000research.com/articles/6-2055). But I also recommend you to have a look at the R DSS package (http://bioconductor.org/...
1
vote
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Toptable error, wont recognize condition
The error is telling you that there isn't any Infected column on the model.matrix.
Check the column names of mm. Also not sure that having two ...
- 4,662
1
vote
Determining what RNAseq data is filtered on volcano plot
edgeR functions do not do any filtering, i.e., they do not remove any genes from the results or from the plots. The only filtering was that which you did explicitly yourself by:
...
- 281
1
vote
the variation between treatments is less than the variation between replicates in RNA-seq data
If you got DEGs with statistically significant p-values after multiple testing following a valid pipeline, I'd believe that, even if looking at the first few PCs doesn't look promising.
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vote
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Voom transformation of RNA seq raw counts data
Try to use DElist() function before you transform, and also make rownames first.
...
- 3,561
1
vote
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How to perform DE analysis for each sample
Treat your disease samples as individual groups and then follow the normal routine in limma to use contrasts to compare two group (the control group versus individual disease samples). Note that you ...
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1
vote
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Correlate DEGs from DESeq2, EdgeR and Limma results
I would use the p-value/FDR which each method returns to rank the gene list for that method in order from 'most likely to be DE' to least likely. You would then have three ranked lists of genes - you ...
- 1,012
1
vote
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Single sample in group: normal pipeline or Kal's Z test
You'll have a bit more power if you use all of the samples at once.
Nothing is useful for this, don't waste your time with it.
You're not looking for outliers, you're looking to see how samples ...
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