Hi Davide,
Lots of good advice here. The main goal with two packages is to
minimize dependencies for the experiment data package as this is
presumed to be the less specialized package. Whenever you have a
package ready please be sure to follow the follow the instructions on
the link that Herve provided.
Thanks in advance for your interest in contributing to the project,
Marc
On 03/04/2013 08:19 AM, Kasper Daniel Hansen wrote:
This is a kind of a chicken and egg problem.
If the data in the experimental data package is in base R containers
(like just a matrix etc), it is pretty clear: the data package does
not depend on anything and the methods package either suggests or
depends on the data package.
However, in most cases, the data will be in some container (S4)
defined in the methods package. In that case I usually let the data
package depends on the methods package and I let the methods package
suggests the data package. Then you need to start each example by
something like
if(require(DATAPACKAGE)) {
CODE
}
I have done this for minfi/minfiData and bsseq/bsseqData
Kasper
On Mon, Mar 4, 2013 at 10:15 AM, Davide Rambaldi<davide.ramba...@ieo.eu> wrote:
You can solve the package size issue by putting your example data in a separate
"experiment data" package
(http://www.bioconductor.org/packages/release/data/experiment/).
Stephanie
I fixed the package size issue with a secondary experiment data package
(flowFitExampleData)
It is not clear to me how to fix the dependencies between the 2 packages:
My setup (I am trying to duplicate the affy/affydata setup…):
flowFit/DESCRIPTION
Suggests: flowFitExampleData
flowFitExampleData/DESCRIPTION
Depends: flowFit
And a lot of (may be are not necessary?)
if (require(flowFItExampleData))
in the examples
It is correct?
Davide
P.S:
tested the package on OSX and Linux with R 3.0 unstable for BUILD and CHECK
and it's OK… (me vs inconsolata.sty: 1 -0)
for windows, well I will try to do it … may be I will ask more help ...
On Feb 27, 2013, at 5:25 PM, Stephanie M. Gogarten wrote:
You can solve the package size issue by putting your example data in a separate
"experiment data" package
(http://www.bioconductor.org/packages/release/data/experiment/).
Stephanie
On 2/27/13 3:03 AM, Davide Rambaldi wrote:
Hi all,
I am working on a library called flowFit, the purpose of this library is to
analyze the FACS data coming from proliferation tracking dyes study.
The library depends on the flowCore and flowViz bioconductor libraries and use
minpack.lm (levenberg-marquadt algorithm) to fit a set of peaks over the FACS
data.
A typical experimental pipeline:
1) Acquire with FACS a sample of unlabelled cells
2) Acquire with FACS a sample of labeled and unstimulated cells (the Parent
Population)
3) Acquire with FACS a sample of labeled and stimulated cells (the
Proliferative Population)
In R we can use the flowCore functions to transform the raw data and to gate
the population of interest. Once we have gated the correct population, with 2
commands of flowFit you can perform the fitting:
library(flowFit)
parent<- parentFitting(QuahAndParish[[1]], "<FITC-A>")
fitting<- proliferationFitting(QuahAndParish[[2]], "<FITC-A>",
parent.fitting.cfse@parentPeakPosition, parent.fitting.cfse@parentPeakSize)
The function can generate also some graphical output with:
plot(fitting.cfse)
To demonstrate the correctness of the fitting I have made some in silico
simulations and a retrospective analysis of the data from the paper:
"New and improved methods for measuring lymphocyte proliferation in vitro and in
vivo using CFSE-like fluorescent dyes", Benjamin J.C. Quah ⁎, Christopher R. Parish,
Journal of Immunological Methods (2012)
In this paper, the same population of lymphocytes (proliferation with the same
growth conditions) was stained with 3 different proliferation tracking dyes: if
the fitting algorithm is working as expected, we expect to estimate the same %
of cells for generation in the 3 sample.
Comparing the 3 samples we didn't see any significant difference in the
estimation of the % of cell for generations, suggesting us that the algorithm
is correctly estimating the % of cells / generation.
I have posted a graphical output example with the Quah and Parish data (pdf)
here:
http://dl.dropbox.com/u/40644496/QuahAndPArishOut.pdf
The dataset will be included in the library (in the data subdir).
Actually I am writing the vignette (I am following the guidelines in
http://www.bioconductor.org/developers/package-guidelines/) and fixing some
graphical bugs (like the legend oversized …).
The package Pass R CMD build and R CMD CHECK (time: 86 seconds) with no errors
on OSX and Linux (I have to find a windows machine somewhere ...), I still have
to test with the R-devel version of R.
The library is bigger than expected (4.2 Mb) because the example datasets (FCS
files converted in .Rdata) are big (3.7M) and I don't know how to solve this
issue...
My question is, How I proceed from here?
I would like to publish the library/methods in a paper (Bioinformatics Journal
may be?) and submit the library to Bioconductor, which is the correct way to
proceed?
Thanks
P.S: If I miss (again!) some FAQ please apologize me
-----------------------------------------------------
Davide Rambaldi, PhD.
-----------------------------------------------------
IEO ~ MolMed
[e] davide.ramba...@ieo.eu
[e] davide.ramba...@gmail.com
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