Hi Felix,

You're rigth, I move to the BioC list.

I tried your exemple, it works well.

In the exptData, do I really need all these fields ? or can I use an empty string for projectPath, fragmentDir, reSequence1 ... ?

Then, just to clarify some points ;

fc@colData is your experimental design, so in your exemple, you have 6 samples from the same viewpoint "testdata".

What about rowData ? if I'm correct, these are the measures (counts) per viewpoint ? so here you only have interacting loci per viewpoint ? Which make sense with the dim of the counts object (6 samples x 2 measures)

I'm just wondering if you can have multiple viewpoints in the same FourC object ? with potentially different number of measures ...

Thank you again,

Best wishes

N.

--------------------------------

Hi Nicolas,

this might be interesting to other users as well. So I would recommend we move the discussion to the Biocondoctor Support Forum if this is fine with you.

Have a look at the script below. This should explain all the steps you need to do to add a custom fragment reference, viewpoint information and counts.

Best regards,
Felix

--------------------------------

library(FourCSeq)

exptData <- SimpleList(projectPath=tempdir(),
                       fragmentDir="re_fragments",
referenceGenomeFile=system.file("extdata/dm3_chr2L_1-6900.fa",
package="FourCSeq"),
                       reSequence1="GATC",
                       reSequence2="CATG",
                       primerFile="",
                       bamFilePath="")

colData <- DataFrame(viewpoint = "testdata",
                     condition = factor(rep(c("WE_68h", "MESO_68h", "WE_34h"),
                                            each=2),
                                        levels = c("WE_68h", "MESO_68h", "WE_34h")),
                     replicate = rep(c(1, 2),
                                     3),
                     bamFile = "",
                     sequencingPrimer="")

fc <- FourC(colData, exptData)
fc

## this step can also be skipped by adding your fragment reference (GRanges)
##rowData(fc) <- your Fragment GRanges
fc <- addFragments(fc)
fc

## counts would by your HiC counts, here I take a random sample
cnts <- matrix(sample(1:10, prod(dim(fc)), replace = TRUE),
               nrow=nrow(fc))
cnts

## make sure they are integers otherwise you will get an error
typeof(cnts)

counts(fc) <- cnts
fc

## manually add viewpoint information because there is no primerfile
colData(fc)$chr = "chr2L"
colData(fc)$start = 6027
colData(fc)$end = 6878

## now you should be able to continue with the workflow

On 10/09/2015 06:34 PM, Nicolas Servant wrote:
Dear Felix,

I'm developing the HiTC package on Bioconductor to analyse Hi-C data.
I would like to make a test to see whether the method implemented in FourCSeq can be used on some specific Hi-C viewpoints (so 4C like information).
So actually, my question is quite simple, let's say that I have a GRanges objects with a count information extracted from HiTC, and that I would like to enter into FourCSeq to detect valid interactions.
I saw that the FourC object require many information about the pre-processing such as the BAM file, etc.
Could give any advices to create such object from a count table ?

Thank you very much
Best wishes
Nicolas