16
votes
Understanding DESeq2 design, contrast and results
The simplest manner is to not use a wald test, but rather an LRT with a reduced model lacking the factor of interest:
...
- 19.5k
10
votes
Accepted
Understanding DESeq2 design, contrast and results
It seems that the "combining factors" trick described in part 3.3 of DESeq2 current "vignette" (as of may 2017) under the title "Interaction" is a way to access to the desired contrasts.
It seems ...
- 3,070
9
votes
Accepted
Removing PCR duplicates in RNA-seq Analysis
For normal RNA-seq PCR duplicates are normally kept in, but the duplication rate can be used as a quality control: The higher the duplication rate, the lower the quality. For expression analysis, it ...
- 3,271
9
votes
What does an FDR value of 1 in RNA-seq mean?
FDR stands for False Discovery Rate. It is a statistic tool used in multiple hypothesis testing.
As you may know, when you use a p-value cutoff (usually 0.05) for your experiments, it means strictly ...
- 191
8
votes
error in heatmap using R
You have scale="col" in the code. What you are plotting is the z-score calculated based on the value distribution per column. Try changing it to ...
- 405
7
votes
differential gene expression complex design no replicates
Mike Love is right. If the response you are looking for is linear in terms of change per minute, the most productive approach is likely to be fitting a linear model. You might get something out of ...
- 3,271
7
votes
Accepted
differential gene expression complex design no replicates
Although it is not recommended to use no replicates, in the edgeR manual they give some advice on how to go on with no replicates design. See page 21 of user guide. You can e.g. estimate a BCV value. ...
- 3,561
6
votes
Accepted
Missing genes and normalisation of RSEM output using EBSeq
Yes, that blog post does represent just one guy's opinion (hi!) and it does date all the way back to 2014, which is, like, decades in genomics years. :-) By the way, there is quite a bit of literature ...
- 5,010
5
votes
Getting a "system is computationally singular" error in sleuth
This happen when the variables (strain +batch) create a design matrix like this:
batch strain
1 1 #
1 1 #
1 2
2 2
3 3
4 3
...
16 72
Which means that some of the ...
- 4,662
5
votes
Accepted
Searching for gene expression data by cell line
Try the Gene Expression Omnibus - it looks like they have some datasets.
5
votes
Accepted
Running differential expression analyses on count matrices with many zeroes
I don't think that the issue is the low counts, but rather the number of features without any real variance (the black dots at the bottom).
So what the heck is the dispersion plot and why does one ...
- 19.5k
5
votes
Accepted
Is removing samples based on clustering for downstream analysis a right choice?
I would be very hesitant to blindly exclude those samples based on the clustering. Check to see if the clusters actually denote some sort of batch effect, since it's not like all of the TCGA datasets ...
- 19.5k
4
votes
Running differential expression analyses on count matrices with many zeroes
Remove low-count features in advance. This is standard for most tools including DESeq2 and edgeR (see section 2.6).
This will keep you from testing a lot of features that cannot be differentially ...
4
votes
Accepted
Differential Expression With 2 Treatments
If you're interested in looking at the differences between two treatments then you'll end up wanting to do both a direct contrast as well as the individual comparisons to baseline.
The direct ...
- 19.5k
4
votes
Comparison of gene set enrichment statistics
I assume that you are talking about the implementation of these methods in the limma package. Otherwise this answer does not apply.
I think that your questions can be answered with some simulations ...
- 4,662
4
votes
Accepted
Performing t.test to obtain p.values for each gene
You can simply apply() t.test() to your matrix. In general, though, I expect lmFit() from ...
- 19.5k
4
votes
Accepted
How to downsample some of the samples in RNA-seq data?
You have a few options:
Downsample the fastq files and rerun the entire analysis. You can do this with seqtk sample.
Downsample the BAM files, which you can do ...
- 19.5k
4
votes
Accepted
The biological meaning of the random variables and the responses in Seurat analysis
In the linked article the authors formalize microarray analysis as the study of the joint distributions of $\overrightarrow{X}_i$ and $Y_i$, where $\overrightarrow{X}_i$ is a vector of random ...
- 3,271
4
votes
Accepted
DESeq2 for large number of samples takes too much RAM
I'll preface this by saying that I don't think DESeq2 is the right tool to use for ATAC-Seq data. My own study of ATAC-Seq patterns [admittedly only a couple of runs that were our first exploration of ...
- 13k
3
votes
How can I specify to DEseq2 to only perform comparisons on which I am interested with?
You can specify the exact comparisons you want in the results() function. So:
...
- 19.5k
3
votes
Removing PCR duplicates in RNA-seq Analysis
Generally you should just leave them as is. One does remove/mark duplicates in DNA seq.
For further read check this Nature paper
- 51
3
votes
Missing genes and normalisation of RSEM output using EBSeq
Include all genes/transcripts in your analysis.
A transcript that is not detected could be undetected through sampling error (i.e. the sequencer / library prep just happened to miss that transcript), ...
- 13k
3
votes
Differential Expression With 2 Treatments
Depending upon the type of treatment used the set of DEGs will change.If the treatments have similar kind of effect you will get a small list (less variable genes will have higher p-val) using a ...
- 594
3
votes
Accepted
Standard Cutoff for Moderated T-statistics
You're misinterpreting the moderated T-statistic, it's basically the fold-change divided by its variance. The p-value comes directly from that, so if you filter by moderated fold-change you're just ...
- 19.5k
3
votes
Accepted
Error in creating a volcano plot in MATLAB
I'm not sure if nowadays matlab is still a good option for informatics. I would rather go for a R script to make a volcano plot. If you don't want to have fancy code but functional one, you can even ...
- 56
3
votes
Accepted
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
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 ...
- 1,806
3
votes
Accepted
Detecting differentially expressed genes with foldchange >= 2 and FDR < 0.05
Fold-change >= 2 is the same as logFC (log2(fold-change)) >= 1, so your example is doing exactly what you want. logFC is generally easier to think in and work with ...
- 19.5k
3
votes
Detecting differentially expressed genes with foldchange >= 2 and FDR < 0.05
The edgeR authors recommend that you use a relatively low logFC threshold for glmTreat such as lfc=log2(1.2). A ...
- 281
3
votes
Accepted
Selection of differential expressed genes
You can usually get away with FDR < 0.1, but that's as high as you can go. This all presumes you're doing follow-up experiments of some sort, of course. I guess you could increase the FDR more, but ...
- 19.5k
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