Hi,
I'm new in microarray data analysis and I have a quick and maybe
"simple"
question about LIMMA and hope some one can help.
We have 6 Agilent data (3 controls and 3 treated samples). I like to
use
LIMMA to figure out differentially expressing genes. I'm confused
about
LIMMA statistics (logFC, AveExpr, t, P.Value, adj.P.Val, and B). Which
one
researchers typically use to select differentially expressing genes?
Can I
simple use adj.P.Val < 0.05 or P.Value < 0.05? Or, should I use
combination
of these statistics? Thanks.
Amy
[[alternative HTML version deleted]]
Hi All,
I want countGenomicOverlaps to output a weighted hit count such that
when a
read maps to, for example four loci, a feature at one of those loci
would
get 1/4th of a count from that read.
At the moment, countGenomicOverlaps doesn't behave the way I expect it
to.
Consider this example:
subj = GRangesList(feature1=GRanges(seq='1', IRanges(10,30),
strand='+'))
qry = GRangesList(read1=GRanges(seq='1',
IRanges(c(10,60,100),c(20,70,110)),
strand='+'))
countGenomicOverlaps(qry, subj, resolution='divide')
I would have expected the hit count to be 1/3 but instead it reports
it as
1/2. Am I using this function correctly?
My sessioninfo is:
R version 2.12.2 (2011-02-25)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GenomicRanges_1.4.0 IRanges_1.10.0
IMPORTANT WARNING: This email (and any attachments) is
...{{dropped:9}}
Hi Mete,
Yes, you are using the function correctly and you have found a bug.
I'll
let you know as soon as it's fixed.
Thanks,
Valerie
On 04/25/2011 04:38 PM, Mete Civelek wrote:
> Hi All,
>
> I want countGenomicOverlaps to output a weighted hit count such that
when a
> read maps to, for example four loci, a feature at one of those loci
would
> get 1/4th of a count from that read.
> At the moment, countGenomicOverlaps doesn't behave the way I expect
it to.
>
> Consider this example:
>
> subj = GRangesList(feature1=GRanges(seq='1', IRanges(10,30),
strand='+'))
> qry = GRangesList(read1=GRanges(seq='1',
IRanges(c(10,60,100),c(20,70,110)),
> strand='+'))
> countGenomicOverlaps(qry, subj, resolution='divide')
>
> I would have expected the hit count to be 1/3 but instead it reports
it as
> 1/2. Am I using this function correctly?
>
> My sessioninfo is:
>
>
> R version 2.12.2 (2011-02-25)
> Platform: x86_64-unknown-linux-gnu (64-bit)
>
> locale:
> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
> [5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
> [9] LC_ADDRESS=C LC_TELEPHONE=C
> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] GenomicRanges_1.4.0 IRanges_1.10.0
>
>
>
> IMPORTANT WARNING: This email (and any attachments) is
...{{dropped:9}}
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
Mete,
The bug is now fixed in the devel trunk (version 1.5.5) and the
release
branch (version 1.4.2). It will be a day before the new package
versions
propagate through the build system and are available through biocLite.
If you want to retrieve them directly they are available via svn at
release :
https://hedgehog.fhcrc.org/bioconductor/branches/RELEASE_2_8/madman/Rp
acks/GenomicRanges
devel :
https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/GenomicRan
ges
I've included an additional example on the man page for
countGenomicOverlaps illustrating the handling of split reads. Let me
know if you run into other problems.
Take care,
Valerie
On 04/26/2011 03:56 PM, Valerie Obenchain wrote:
> Hi Mete,
>
> Yes, you are using the function correctly and you have found a bug.
> I'll let you know as soon as it's fixed.
>
> Thanks,
> Valerie
>
>
> On 04/25/2011 04:38 PM, Mete Civelek wrote:
>> Hi All,
>>
>> I want countGenomicOverlaps to output a weighted hit count such
that
>> when a
>> read maps to, for example four loci, a feature at one of those loci
>> would
>> get 1/4th of a count from that read.
>> At the moment, countGenomicOverlaps doesn't behave the way I expect
>> it to.
>>
>> Consider this example:
>>
>> subj = GRangesList(feature1=GRanges(seq='1', IRanges(10,30),
>> strand='+'))
>> qry = GRangesList(read1=GRanges(seq='1',
>> IRanges(c(10,60,100),c(20,70,110)),
>> strand='+'))
>> countGenomicOverlaps(qry, subj, resolution='divide')
>>
>> I would have expected the hit count to be 1/3 but instead it
reports
>> it as
>> 1/2. Am I using this function correctly?
>>
>> My sessioninfo is:
>>
>>
>> R version 2.12.2 (2011-02-25)
>> Platform: x86_64-unknown-linux-gnu (64-bit)
>>
>> locale:
>> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
>> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
>> [5] LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
>> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
>> [9] LC_ADDRESS=C LC_TELEPHONE=C
>> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>
>> attached base packages:
>> [1] stats graphics grDevices utils datasets methods
base
>>
>> other attached packages:
>> [1] GenomicRanges_1.4.0 IRanges_1.10.0
>>
>>
>>
>> IMPORTANT WARNING: This email (and any attachments) is
...{{dropped:9}}
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
On Mon, Apr 25, 2011 at 6:30 PM, Amy Johnson <a7johnson at="" gmail.com="">
wrote:
> Hi,
>
> I'm new in microarray data analysis and I have a quick and maybe
"simple"
> question about LIMMA and hope some one can help.
>
> We have 6 Agilent data (3 controls and 3 treated samples). I like to
use
> LIMMA to figure out differentially expressing genes. I'm confused
about
> LIMMA statistics (logFC, AveExpr, t, P.Value, adj.P.Val, and B).
Which one
> researchers typically use to select differentially expressing genes?
Can I
> simple use adj.P.Val < 0.05 or P.Value < 0.05? Or, should I use
combination
> of these statistics? Thanks.
Hi, Amy. Your best bet is to thoroughly read the Limma User Guide and
the help pages for ALL the commands you used to generate your topTable
results. Also, it will help to get some basics of statistics under
your belt. There are no hard-and-fast rules about what should be
used, but many folks will use adj.P.Val (adjusted to be a False
Discovery Rate) with or without logFC cutoffs.
Sean
