Hi, 

I went through the whole workflow and I would like to print tables with the data in results and fcf objects. How can I do that?

One table could include sample wise information with zscores, p-values, counts and the other with the differences between conditions. I have 2 reps for each of the 3 conditions. 

Ideally, I would like to see the peaks/interactions with chr and coordinates (chr, start, end).

Is there an easy way to add the closest or overlapped feature in the genome? using TxDb.Hsapiens.UCSC.hg19.knownGene?

As far I understand this method only looks at interactions within the same chromosome (no trans interactions) right?

I am not an expert on R as you noticed.

Thank you very much for your time and help,

Francesc

Hello Francesc,

as first step I would recommend to look into the pdf vignette of the package. There are some examples how the data can be extracted.

Here is a short summary of the things I think you are looking for:

You can extract the genomic information about the fragments (GRanges Object) with the following command:

frags <- rowData(fcf)

This contains quite some information from intermediate steps. If you only want the positions use this:

mcols(frags) <- NULL

To extract the zscores (as matrix) from the Object run this command:

zScore <- assay(fcf, "zScore")

And to merge it with the genomic positions it has to be transformed to a DataFrame and then can be attached to mcols of the GRanges object.

mcols(frags) <- cbind(mcols(frags), DataFrame(zScore))

frags

For the other assays you can do this as well. Which assays there are can be checked by this:

names(assays(fcf))

For the differential analysis you get the results like this:

results <- getAllResults(fcf)
head(results)[,1:6]

I hope this answers your questions. There are some nice tutorials how to work with GRanges and SummarizedExperiments (which is the class on which FourCSeq builds on). Have a look at those as well to get more familiar with them

Best regards,

Felix

Thanks for all your help. 

I used this to annotated with genes that overlapped the coordinates.

Mapping genome regions to gene symbols

Hi Felix I tried doing what you advised with combinedData <- cbind(as.data.frame(rowRanges(fcf)), results) 

and also with mcols(frags) <- cbind(mcols(frags), DataFrame(zScore))  but I get the error:

Error in DataFrame(..., check.names = FALSE) : 
  different row counts implied by arguments

Do you know how can I solve this?

Thanks!