A very large number of diffential genes
6
0
Entering edit mode
GFM ▴ 20
@gfm-8326
Last seen 6 months ago
European Union

Hello,

We ran DESeq2 on an experiment with several samples (each sample in triplicates). The samples differ dramatically. In one of the pair wise comparisons, we have ~4200 differentially expressed genes (with fold change above 2 or below -2, p-adj<0.05), out of ~19,000 detected genes (human genome).
DESeq normalization is based on the assumption that most genes are not differentially expressed.
Do you think that it is valid to use this normalization in this case?

Thank you

deseq2 normalization • 933 views
ADD COMMENT
1
Entering edit mode
GFM ▴ 20
@gfm-8326
Last seen 6 months ago
European Union

Thanks a lot for the answer! I am pasting below an image of the MA plot (I hope it is OK to add an image and it will appear OK).
I think it is OK. Do you think that the normalization is OK? (DESeq was run with beta prior False).

 

ADD COMMENT
0
Entering edit mode
@mikelove
Last seen 17 hours ago
United States

That's not exactly the assumption. It's that the median ratio captures the sequencing depth differences. So there are many scenarios where you could have many DEG and still have good normalization using the standard size factor estimates.

Take a look at plotMA(res) to see if there are genes with large mean count falling along the x axis.

ADD COMMENT
0
Entering edit mode
GFM ▴ 20
@gfm-8326
Last seen 6 months ago
European Union

Thanks a lot for the answer!
Here is a link to the MA plot:
https://weizmann.box.com/s/dtd75ielrfxxf6vvek3ziuk6tel27a8p

I think it looks OK. Do you think that the normalization is OK? (DESeq2 was run with beta prior False).

ADD COMMENT
0
Entering edit mode

It's hard to say, can you zoom out on the yaxis (ylim=c(-10,10)) ?

Are these technical replicates or biological replicates? Can you say more about the experiment? If nearly all genes show differential expression, it can become difficult or not possible to normalize using computational methods alone.

ADD REPLY
0
Entering edit mode
GFM ▴ 20
@gfm-8326
Last seen 6 months ago
European Union

Thanks!
Here is a link to the zoomed out MAplot:

https://weizmann.box.com/s/vx9djse9s4fbgnwr373h03kbby0opfc9

 

 

ADD COMMENT
0
Entering edit mode

The normalization looks fine to me (the horizontal line goes through the middle of the points).

My interpretation is that the biological replicates are very similar to each other and very different across condition. You may want to use lfcThreshold (see vignette) to come up with lists of genes which change more than a threshold which makes sense for your experiment, given that many genes show a consistent difference across condition.

ADD REPLY
0
Entering edit mode
Aedin Culhane ▴ 510
@aedin-culhane-1526
Last seen 21 months ago
United States

I have seen large number of differentially expressed genes if 

1) Experimental design, for example there is a batch effect that is confounded with the biological effect, for example all controls were performed in one batch and all the treatment were performed in a separate batch. In this case the differentially expressed genes could be due to the biological or batch effect and there is no way to determine which is which.  

2) Replicate issues. Are your replicates technical or biological

3) Its true (rarer) ! Maybe you really have a very large biological effect.  For example estrogen receptor positive v negative breast cancer is very different, by about 2,000 genes. Or you are disrupting a fundamental cell pathway, for example an key gene in transcription (polII) and thus lots of genes are affected.  Have a look at the genes that are differentially regulated, do they make sense in your cell system.  

Aedin

 

 

 

ADD COMMENT
0
Entering edit mode
GFM ▴ 20
@gfm-8326
Last seen 6 months ago
European Union

Thanks a lot for the response!
The replicates are biological and not technical (and they cluster very nicely). The samples are very different biologically, so we do expect to have lots of changes. I am just wondering whether in such a case DESeq's normalization is valid.

ADD COMMENT

Login before adding your answer.

Traffic: 229 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6