Error : package slot missing from signature for generic

Dear guRu's,
please help me escape dependency hell...
in my package foo, i'm trying to replace the 'sampleNames<-' method from package lumi. This works well, ie I
can install foo, and execute the updated code.

However, if my package foo imports oligoClasses, (or as it turns out, a chain of dependencies which lead to
oligoClasses), I get this error during INSTALL
* installing to library ‘/Library/Frameworks/R.framework/Versions/2.15/Resources/library’
* installing *source* package ‘foo’ ...
** R
** preparing package for lazy loading
Warning: replacing previous import ‘image’ when loading ‘graphics’
Warning: replacing previous import ‘density’ when loading ‘stats’
Warning: replacing previous import ‘residuals’ when loading ‘stats’
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded
*** arch - i386
Warning: replacing previous import ‘image’ when loading ‘graphics’
Warning: replacing previous import ‘density’ when loading ‘stats’
Warning: replacing previous import ‘residuals’ when loading ‘stats’
Error : package slot missing from signature for generic ‘sampleNames<-’
and classes LumiBatch, ANY
cannot use with duplicate class names (the package may need to be re-installed)
Error: loading failed
Execution halted
*** arch - x86_64
Warning: replacing previous import ‘image’ when loading ‘graphics’
Warning: replacing previous import ‘density’ when loading ‘stats’

read.maimages

Dear Assa,

It seems to me that the read.maimages() help page

   help("read.maimages")

answers your question.  The help page, for the version of limma that you 
are using, says

"In the case of Agilent and GenePix, two possible foreground estimators are 
supported: source="genepix" uses the mean foreground estimates while 
source="genepix.median" uses median foreground estimates. Similarly for 
Agilent."

So the help page tells you that read.maimages() reads the mean foreground 
by default, not the median foreground as you say in your email.  So if you 
override the default by reading in the median foreground, it is clear that 
you will get differing results.

If you were to upgrade to the current version of R and the current version 
of limma, there much expanded documentation about reading Agilent files in 
the User's Guide (and the default for agilent has changed).

Please note, I am happy to answer questions about current limma 
documentation.  However, if you follow a third party website that gives 
advice conflicting with the limma documentation, then you should send 
questions to the author of that website.

Best wishes
Gordon

edgeR, error in estimateCommonDisp

Dear Lucia,

This is a failure in the edgeR function estimatePs(), and it is something 
that I have never seen before.

Have you checked that your input counts are as you expect?  For example,

   summary(y$counts)

You don't say what version of edgeR you are using.  Could you please try 
installing the devel version of edgeR, and tell me whether the problem has 
gone away?

Best wishes
Gordon

PS. See the advice about sessionInfo() in the posting guide:

  http://www.bioconductor.org/help/mailing-list/posting-guide/

> Date: Mon, 30 Jul 2012 14:17:31 -0400
> From: Lucia Peixoto <luciap@...>
> To: bioconductor@...
> Subject: [BioC]  edgeR, error in estimateCommonDisp
>
> Hi All,
> I am pretty new to edgeR, this is the first time I use the package, as
> well as the first time I try to find differential expression using RNA-Seq
> data (this is the first time I have biological replicates).
> I apologize in advance for my lack of knowledge.

edgeR- data for case studies

Dear Franklin,

You don't say what version of edgeR you are using, but the latest 
version of the User's Guide

http://bioconductor.org/packages/2.11/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf

explains explicitly how to get every dataset for yourself.  The source of 
the dataset is explained at the start of every case study.

You have to download these datasets for yourself.  We don't ship them with 
the edgeR package.

Best wishes
Gordon

> Date: Sun, 29 Jul 2012 19:15:27 +0000
> From: "Johnson, Franklin Theodore" <franklin.johnson@...>
> To: "bioconductor@..." <bioconductor@...>
> Subject: [BioC] edgeR- data for case studies
>
> Hello,
>
> I am trying to repeat the edgeR process for section 3.3, 3.4 and 3.5 of 
> the edgeR user guide.
>
> However, in the edgeR data folder, labeled 'data', there are only four 
> .txt files. These .txt files are used to implement edgeR analysis the 
> SAGE experiment for sections 3.1 and 3.2 only.
>

general question about omogeneity of variances between microarray groups

Dear Guido,

> Date: Tue, 31 Jul 2012 11:13:20 +0200
> From: Guido Leoni <guido.leoni@...>
> To: <bioconductor@...>
> Subject: [BioC] general question about omogeneity of variances between
> 	microarray groups
>
> Dear list
> I'm performing some microarrays analysis for a simple case(15 microarrays)
> , control(3 microarrays) experiment design.
> Don't ask me the reason for which i have a so unbalanced dataset 
> In order to detect differentially expressed genes I wish to perform a LIMMA
> analysis...but checking the omogeneity of variances with bartlett test I
> observ a difference statistically significative between cases and controls.
> According to your experience:
> Is a good idea before doing a parametric analysis checking the variances
> utilizing Bartlett test?

No, it is a very bad idea.  Bartlett's test is well known to be highly 
sensitive to non-normality, so it is very likely to give significant 
results as a result of small deviations from normality rather than genuine 
differences in variances.  By contrast, the two-sided t-test that limma 
does is quite robust against both non-normality and inequality of 
variances.

George Box had a few choice words more than half a century ago for what 
you propose.  He said it was like setting out in a rowing boat to 
check if the ocean was calm enough for an ocean liner.  See for example:

Re: read.maimages

Hi Gordon,

Dear Assa,
>
> It seems to me that the read.maimages() help page
>
>   help("read.maimages")
>
> answers your question.  The help page, for the version of limma that you
> are using, says
>
> "In the case of Agilent and GenePix, two possible foreground estimators
> are supported: source="genepix" uses the mean foreground estimates while
> source="genepix.median" uses median foreground estimates. Similarly for
> Agilent."
>

I have updated to the latest version of R (2.15.1) and limma (3.12.1).
I should have read the help page. But I read the User's manual (I think the
latest version - from June, 10th 2012).
The manual says differently (page 18):
the default values for agilent are not the same as for genepix.
by just stating 'source="agilent" ', limma takes both fore- and background
median signals.

So maybe the manual needs an update.

> So the help page tells you that read.maimages() reads the mean foreground
> by default, not the median foreground as you say in your email.  So if you
> override the default by reading in the median foreground, it is clear that

Re: read.maimages

Dear Assa,

No, the User's Guide is correct.  As I said in my last email, the default 
for agilent has changed between the older version that you were using and 
the current version.  The documentation with each version of limma 
correctly described the behaviour of that version of the software.

Best wishes
Gordon

On Wed, 1 Aug 2012, Assa Yeroslaviz wrote:

> Hi Gordon,
>
> Dear Assa,
>>
>> It seems to me that the read.maimages() help page
>>
>>   help("read.maimages")
>>
>> answers your question.  The help page, for the version of limma that you
>> are using, says
>>
>> "In the case of Agilent and GenePix, two possible foreground estimators
>> are supported: source="genepix" uses the mean foreground estimates while
>> source="genepix.median" uses median foreground estimates. Similarly for
>> Agilent."
>>
>
>

Post-Doc - Milan: miRNAs and non-coding RNAs identification from RNAseq data

*Post-Doc - Milan: miRNAs and non-coding RNAs identification from RNAseq
data*

A position in computational biology is available at the European Institute
of Oncology (IEO).

Our group is engaged in a number of projects in areas of cancer genomics,
development and stem cells. These projects include studying the role of
microRNAs and miRNA:mRNA networks during development, in cancer, and in
adult stem cells, as well as investigating microRNAs (miRNAs) and
non-coding RNAs (ncRNAs) as biomarkers for cancer and disease.

Candidate will have the opportunity to exploit genomic analysis of miRNAs
and ncRNAs from tumors samples searching for biomarkers for: i) cancer risk
prediction and/or early diagnosis; ii) detection of minimal residual
disease (MRD) and/or monitoring response to therapy; iii) cancer prognosis.

The position entails application of computational tools for the analysis
and interpretation of high-throughput genomic data, primarily sequencing
data (NGS, Illumina), and meta-analysis of cancer datasets of miRNA/mRNA
expression profiling. Prior experience in processing and managing
high-throughput sequencing data is desired.

Applications will be accepted until the position is filled. Please submit a
CV (max 3 pages), a brief research statement (max 1 page), and names of at
least two references to Francesco Nicassio (francesco.nicassio@...).

Please quote: “position_bioinfo” in the subject field of your e-mail.

*Keywords:* RNAseq, miRNA, non-coding, cancer

Re: DiffBind error loading dba.count

Hi Gordon,
I tried test=dba.count(test,bParallel=F) but still facing the same
problem, it seems that 6 Gb of RAM are not enough to load
2 GB of bed file, is it possibile?
I suggest I should switch to a bigger machine..
paolo

2012/7/31 Gordon Brown <Gordon.Brown@...>:
> Hi, Paolo,
>
> DiffBind uses quite a lot of memory for the dba.count step, because it loads
> the whole data file into memory.  (In hindsight, not the best design
> choice...)  Try running with parallelization turned off:
>
>> test=dba.count(test,bParallel=F)
>
> so that it only loads one file at a time, to reduce the memory footprint.
> For the upcoming release, we'll probably fix this, at the cost of somewhat
> slower counting.
>
> How much RAM does your machine have?
>
> Cheers,
>
>  - Gord
>
>
> On 2012/07/30 11:00, "Paolo Kunderfranco" <paolo.kunderfranco@...>
> wrote:
>

DESeq normalization

Hi all,

I was checking DESeq normalization success for my samples following the suggestions in this post :
https://stat.ethz.ch/pipermail/bioconductor/2010-October/035933.html. I observed that
filtering down to the genes that are present in both case and control samples improved normalization 
(visualized by plotting as per the post), but for a few samples ,say  3 samples these procedures did not
help, and I tried calculating shorth estimator (again as per the post) and this did n't help either. My
question is what should I follow for the normalization of these samples ?

Regards,
Sudeep.

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