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5 votes
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Removing Batch Effect in Heatmaps after Differential Gene Expression Analysis

It is indeed true that for the DE analysis one should include batch into the formula to avoid changing the original counts. Still, for everything else such as plotting heatmaps use of ...
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4 votes

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 ...
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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: ...
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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 votes

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 ...
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3 votes
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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 ...
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2 votes

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 votes

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 votes

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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2 votes
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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: ...
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2 votes
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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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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 ...
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2 votes
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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 ...
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1 vote

What is the difference between Normalized Expression in EdgeR vs DESeq2?

First, and most obviously, two independent methods will never fully agree. Second, while the DESeq2-related code in fact gets the normalized counts based on the size factors the edgeR/voom code ...
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1 vote
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Generating contrast matrix for limma in loop

I wrote this snippet a while back to generate all pairwise contrasts: ...
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  • 2,706
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/...
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1 vote

How is the t-statistic value calculated in GEO2R or Bioconductor?

That is not trivial. GEO2R uses the Bayesian linear model-based framework limma https://bioconductor.org/packages/release/bioc/html/limma.html for the analysis. I ...
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1 vote
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Details of DESeq2 modeling a batch effect

DESeq2 uses the batch information (and everything else in the design) to produce offsets for its GLM. For a background on that please check how linear models work, e.g. using the StatQuest series of ...
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  • 2,706
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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1 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. ...
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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 ...
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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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