14 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: ...
user avatar
  • 19.2k
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 ...
user avatar
  • 3,211
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 ...
user avatar
  • 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 ...
user avatar
  • 395
8 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 ...
user avatar
  • 2,960
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 ...
user avatar
  • 3,211
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. ...
user avatar
  • 3,541
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 ...
user avatar
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 ...
user avatar
  • 4,602
5 votes
Accepted

Searching for gene expression data by cell line

Try the Gene Expression Omnibus - it looks like they have some datasets.
user avatar
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 ...
user avatar
  • 19.2k
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 ...
user avatar
  • 19.2k
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 ...
user avatar
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 ...
user avatar
  • 19.2k
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 ...
user avatar
  • 4,602
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 ...
user avatar
  • 19.2k
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 ...
user avatar
  • 3,211
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: ...
user avatar
  • 19.2k
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
user avatar
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), ...
user avatar
  • 11.7k
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 ...
user avatar
  • 584
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 ...
user avatar
  • 19.2k
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 ...
user avatar
  • 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 ...
user avatar
  • 4,602
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 ...
user avatar
  • 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 ...
user avatar
  • 19.2k
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 ...
user avatar
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 ...
user avatar
  • 19.2k
3 votes

How to normalise scRNASeq data for differential expression analysis

I would suggest using a likelihood ratio test for differential expression using logistic regression with batch as a latent variable. In Seurat you can do: ...
user avatar
  • 674
3 votes
Accepted

How I deal with this kind of gene expression comparision

No. If the only difference between Tumour_batch1 and Tumour_batch2 was the library prep/sequencing run, then all this will tell you is if the particular sample of RNA taken from the tumour is ...
user avatar
  • 3,211

Only top scored, non community-wiki answers of a minimum length are eligible