Getting up and downregulated genes in cummeRbund

[Galgarad]

Hi folks,

I've been getting a lot of awsers from this forum when I searched Google for many bioinformatics questions I had. I wonder if you guys could help me with this one.

I've run cuffdiff and I managed to get a bit done in cummeRbund (i'm totally new to R..). Now, I've been able to get the differentially expressed genes with this line :

mySigGenes <- getSig(cuff, alpha = 0.05, level = "genes")

However, I want to further split this group in upregulated and downregulated genes, either using the raw fpkm or the log2 numbers..

Can we do that?

Thanks!

[Galgarad]

Anyone?

Is this only an "R" language issue that I can't seem to understand?

[IBseq]

Hi Galgarad,
I need a similar thing and what I have done (it might be wrong) was first to extrapolate all the lanes which were significant. I shall say I am doing all this on excel.
I then sorted the values for my normal sample from the highest to the smallest and in this way I could see the downregulated genes in the treated sample.
I then did the opposite, I sorted the treated sample the same way, so that I could visualise the overexpressed genes. in both cases, when u sort in excel one column, those close to it are arranged as well (you can choose not to , but you do want it).

I shall say that I dont know if this a proper way: one rule I put (a former phd student in my lab did) was to consider FPKM values above 200, in this way i did not have many genes to consider. I am new to R, and still waitingto have unix/linux access to download all necessary packages. hope it helps.

a quick question in case anyone can answer@ above which fpkm value do we consider a gene/transcript over expressed or not?I used this value of 200, but i do not have any evidence for this

Thanks
irene

[DZhang]

Hi Galgarad,

Since you are new to R, I believe IBseq's approach probably works best for you. In R, save out the list of differentiallly expressed genes in a text file, and then filter out everything in excel.

As to your original question, yes, it is purely a R thing. You simply need to pass a condition to R to filter the results in the column of read counts or fold change.

Douglas

https://www.contigexpress.com

[IBseq]

Hi folks,

I've been getting a lot of awsers from this forum when I searched Google for many bioinformatics questions I had. I wonder if you guys could help me with this one.

I've run cuffdiff and I managed to get a bit done in cummeRbund (i'm totally new to R..). Now, I've been able to get the differentially expressed genes with this line :

mySigGenes <- getSig(cuff, alpha = 0.05, level = "genes")

However, I want to further split this group in upregulated and downregulated genes, either using the raw fpkm or the log2 numbers..

Can we do that?

Thanks!

Hi Galgarad,
after a thought I have done differtly my analysis.
To give a cut off value of 200 is not valid and it is arbitrary. So I calculated the fold change between the values given and then used the highest one for my analysis.

hope it helps,
ib

[Galgarad]

Thanks to all.

@IBseq

I had already done these kind on things with excel. But what I want to do, is to use cummeRbund to make analysis on subset of genes, i.e. upregulated vs downregulated genes. And since cummeRbund takes all the files from cuffdiff as input, doing so doesn't enable me to use cummeRbund.

@DZhang

In fact, getting to use cummeRbund with all the differently expressed genes is pretty straight forward (since it's clearly indicated in the manual). But once I have the list of differentially expressed genes in cummeRbund, do you have an idea of how I could further split it in up or down regulated genes? With some kind of condition like : if fpkm 1 < fpkm 2, gene is upregulated ?

Thanks a ton!

[DZhang]

Hi Galgarad,

Please get this paper Nature Protocols, Vol 7; No. 3; 562-578. On Page 575, it contains the actual R commands to write out a simple text containing a list of differentially expressed genes with (significant == Yes). Depending on your programming level, you may change those commands to filter for down or up regulated genes or you may write out the list and open it in Excel. I usually advise to work it in Excel as it is much more comfortable for most researchers.

Best regards,
Douglas

https://www.contigexpress.com