Hi there!!!
I have did this code:
SampleTable <-data.frame(SampleName=metadata$ID_CLINICO,fileName=metadata$NOME,
condition=metadata$CONDITION,prim=metadata$CDT)
ddHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable=SampleTable,directory="
Count/", design= ~condition) # effetto dello mutazione
ddHTSeq
hi Jarod,
On Thu, Jul 10, 2014 at 7:59 AM, jarod...@libero.it wrote:
> Hi there!!!
>
> I have did this code:
> SampleTable
> <-data.frame(SampleName=metadata$ID_CLINICO,fileName=metadata$NOME,
> condition=metadata$CONDITION,prim=metadata$CDT)
> ddHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable=S
Dear Bioc developers,
If you have not already seen the July Bioconductor newsletter, it is
worth taking a look, specifically the section about Mac OS X Mavericks
adoption:
http://www.bioconductor.org/help/newsletters/2014_July/#new-mac-os-x-mavericks-build-machines
As you probably know, the re
a new, more inclusive GWAS catalog is available (GRASP, from Andrew Johnson
at NHLBI), with 6 million records and voluminous metadata (though it seems
sparse and perhaps can be trimmed/reshaped)
i made a GRanges and it takes 3 minutes to load. even after stripping all
the
metadata, a GRanges with
Hi,
On Thu, Jul 10, 2014 at 1:52 PM, Vincent Carey
wrote:
> a new, more inclusive GWAS catalog is available (GRASP, from Andrew Johnson
> at NHLBI), with 6 million records and voluminous metadata (though it seems
> sparse and perhaps can be trimmed/reshaped)
>
> i made a GRanges and it takes 3 mi
On Thu, Jul 10, 2014 at 2:16 PM, Steve Lianoglou
wrote:
> Hi,
>
> On Thu, Jul 10, 2014 at 1:52 PM, Vincent Carey
> wrote:
> > a new, more inclusive GWAS catalog is available (GRASP, from Andrew
> Johnson
> > at NHLBI), with 6 million records and voluminous metadata (though it
> seems
> > sparse
On Thu, Jul 10, 2014 at 7:05 PM, Michael Lawrence wrote:
>
>
>
> On Thu, Jul 10, 2014 at 2:16 PM, Steve Lianoglou > wrote:
>
>> Hi,
>>
>> On Thu, Jul 10, 2014 at 1:52 PM, Vincent Carey
>> wrote:
>> > a new, more inclusive GWAS catalog is available (GRASP, from Andrew
>> Johnson
>> > at NHLBI),
Hello,
When doing a gene ontology analysis, it is common to remove gene sets that are
small to avoid a problem of their statistics spuriously being significant. For
example, I have a list of 681 significantly differentially expressed genes, but
some expected overlap values are much smaller than
