DESeq2 questions

[SDPA_Pet]

Here is the bioconductor support site. Tag your question with deseq2.

Hi GenoMax,

Thanks. BTW, do you know is there any good forum to discuss R application. Bioconductor.org focus on molecular bioinformatics. I need some forums such as help people with plots (ggplots 2), multivariate statistics, etc.

[GenoMax]

Can you give the link? I google it and find different sites. Not sure which one is the authentic

Here is the bioconductor support site. Tag your question with deseq2.

[SDPA_Pet]

You may want to post this question on Bioconductor DESeq2 support site where people with the statistical chops will answer (someone on here may do that as well).

Can you give the link? I google it and find different sites. Not sure which one is the authentic

[GenoMax]

You may want to post this question on Bioconductor DESeq2 support site where people with the statistical chops will answer (someone on here may do that as well).

[SDPA_Pet]

Right, my questions about the dataset choosing.

In you example, let say DNA_systhesis total have 50 counts. I can go deeper hierarchical level
DNA systhesis

sub Function 1 5
sub function 2 3
...

TCA total 100
sub Fucition 1 5
sub fuction 2 3

....

I could choose two way to do this. First, use the total datasets which is 150 functions total, and look at which sub function related to DNA synthesis is overabundant.

Or, I could only extract the 50 subfunctions from DNA synthesis and run DEseq to find out which one is overabundant.

To me, I only care about one large category. However, the sample size wouldn't be same in the two different analyses

DESeq2 is not operating on the identifiers but is considering the counts associated with them.

DNA_synthesis      50
TCA_Cycle        100

OR

BRCA1      50
TRPV1        100

should produce equivalent results from DESeq2.

[GenoMax]

DESeq2 is not operating on the identifiers but is considering the counts associated with them.

DNA_synthesis      50
TCA_Cycle        100

OR

BRCA1      50
TRPV1        100

should produce equivalent results from DESeq2.

[SDPA_Pet]

@SDPA_Pet: I think you have metagenomics data that you have somehow mapped as counts --> a function (so not a specific gene). Is that interpretation correct? That may be important for other posters to know.

In any case cherry picking data is not the way to go with DESeq2.

Right? It's kind of tricky? In our field, we the conception of gene/functions are exchangeable.Anyhow, it is just counts table.

Just curious,GenoMax? What is difference between the gene table and function table in your field (I would guess your field is biomedical field)? Do you have an example?

[GenoMax]

@SDPA_Pet: I think you have metagenomics data that you have somehow mapped as counts --> a function (so not a specific gene). Is that interpretation correct? That may be important for other posters to know.

In any case cherry picking data is not the way to go with DESeq2.

[SDPA_Pet]

DESeq2 expects raw read-level count data as input. I don't expect that it would be possible to get that from a function data set.

Hey guys, I am sorry about the confusion. The dataset that I use the gene counts dataset. (The gene coding for the functional genes).

So, should I use the subset or total dataset.

[gringer]

DESeq2 expects raw read-level count data as input. I don't expect that it would be possible to get that from a function data set.

[wdecoster]

Can I just use the functions related to DNA synthesis (subset table) to do the DESeq2 analysis or I have to use the whole dataset and later found which over-representative functions relative to DNA synthesis?

You should use the whole dataset and later on check if the genes up and down regulated correspond to a certain function.

[SDPA_Pet]

Are you mixing up differential expression analysis (DESeq2) with GO term enrichment analysis (e.g. GeneSCF)?

Well, I don't think so, because DESeq2 can give you log2 fold change of each functions and whether the change significant or not.

[GenoMax]

Are you mixing up differential expression analysis (DESeq2) with GO term enrichment analysis (e.g. GeneSCF)?

[SDPA_Pet]

Hello, I have a function data sets. I want to look at which functions are over-representative. However, I am only interested in a subset of functions for example DNA synthesis.

Can I just use the functions related to DNA synthesis (subset table) to do the DESeq2 analysis or I have to use the whole dataset and later found which over-representative functions relative to DNA synthesis?

Thanks,