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7 votes
Accepted

How is prior.count used by edgeR's cpm

The prior count ends up getting scaled by the ratio of a library size to the average library size and then multiplied by 2 before getting added to each library size (I'm sure there's a good reason for ...
  • 19.3k
6 votes
Accepted

When performing differential expression analysis, should genes with low read counts be removed before or after normalization?

The more genes you have the more robust the scaling factor is (among the reason why one doesn't normalize to ERCC spike-ins without a compelling reason), so I suppose in theory it's better to filter ...
  • 19.3k
5 votes
Accepted

What do these files / annotations mean?

You are looking at the supplementary data of a paper. That seems to have given you a list of features, and some information about those features. Specifically, you seem to have a list of two types of ...
  • 8,225
4 votes

Error in quantile.default(x, p = p) in EgdeR calcNormFactors

Ok, it turned out that some rows and/or columns contained all values as zeros and that was the reason for the error. I wrote a function to fix that: ...
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 ...
  • 1,648
4 votes

Why use "robust" FPKMs?

FPKM are inherently experiment specific and can not be used to compare across samples. Let's consider the following two sequencing runs. Let $E1$ and $E2$ be the true, underlying expression in two ...
4 votes
Accepted

How to incorportate RIN values as covariate in the design matrix?

In the DESeq2 manual there is a section titled "How can I include a continuous covariate in the design formula?" that deals with your question. Basically the process is no different from using a ...
  • 818
3 votes

Basic RNA Differential Expression in R

See here for a Differential Expression guide which discusses carrying out differential expression using DESeq2: https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html ...
  • 12k
3 votes

Experimental Design for Differential expreression analysis

You're really running out of degrees of freedom and the only actual replicate is a single sample from another patient, so please take the results with a huge grain of salt. You can use a design of <...
  • 19.3k
3 votes

*very* unbalanced group sizes for DE

In general, you would expect roughly the same distribution on both sides of your volcano plot. The first two plots are concerning since there's so much on the right and not on the left. With such a ...
  • 131
3 votes

*very* unbalanced group sizes for DE

Random partitioning (as you have done) seems like a reasonable thing to do to work out what's going on. From the results you have suggested, it looks like DESeq2 is performing better than EdgeR, so ...
  • 12k
3 votes

Batch Effects in RNA Seq Sample

The help page for removeBatchEffect() explicitly states that it should not be used in conjunction with differential expression testing, so don't do that. You ...
  • 19.3k
3 votes

How to calculate logCPM across all samples?

If you want the values by group then subset y to contain the samples of interest and then feed that to aveLogCPM().
  • 19.3k
2 votes

What could be the reason for the samples not clustering?

Samples will only cluster by experimental group if the experimental effect is large enough that it's the primary source of variance between your samples. If that's not the case then you'll get results ...
  • 19.3k
2 votes

confidence ellipses for MDS plot in edgeR?

I can see that there would be a three step process to doing this: Merge counts from all samples in the group and then resample pseudo-replicates from this. If x is ...
  • 3,221
2 votes
Accepted

RNA-seq: How to get new expression count after normalization

If I'm interested to get normalized gene counts, can I go ahead and multiple each individual's norm.factor into its gene counts? For example, the expected count for IND1 for Gene1 is 100 and it's ...
  • 1,722
2 votes
Accepted

What is the difference between AnnotationDbi/org.Mm.eg.db and biomaRt/Mus.musculus for converting to gene symbols?

AnnotationDBi uses AnnotationDb objects which is the virtual base class for all annotation packages. It contain a database ...
2 votes
Accepted

plotting gene expression after EdgeR DE analysis using RUVg (RUVseq) covariates

You could correct your logCPMs (edgeR::cpm(y, log=TRUE)) with the covariates from RUVseq using limma and then use these for plotting: ...
1 vote

Basic RNA Differential Expression in R

I'm thinking first normalize using TMM then compare the mean of the normalized values for treated vs. untreated? That is a bit too simple. You need to analyse your experiment as a paired comparison. ...
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.
  • 1,722
1 vote

RNASeq analysis using featureCount and EdgeR

It makes no difference if you process the BAM files one at a time with featureCounts or all together, except that it changes how you have to read the files into R. You can supply edgeR with lists of ...
  • 19.3k
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: ...
1 vote

Bulk RNA-Seq Read Length Normalization across different samples

The simplest thing to do is to trim the 150 bp fasts so that they are 100 long. I don't think there is an easy way to correct for the fact that the 150 bp long reads will have a higher unambiguous ...
  • 1,722
1 vote
Accepted

How to create a list of differentially expressed (DE) genes after normalization with RUVSeq?

You do the normalization before running your edgeR. The purpose of RUVg is to remove "Remove Unwanted Variation Using Control Genes". In your code, you ...
  • 1,648
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

What do these files / annotations mean?

gi looks like a Genbank Identifier. You can search for the 10 digit number following gi| at the Genbank website.
1 vote
Accepted

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 vote
Accepted

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 ...
  • 19.3k

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