Closed BioSok closed 6 years ago
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Dear Package contributor,
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Your package has been built on Linux, Mac, and Windows.
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Hi,
I've done a first review. A few comments and suggestions below. The deadline for getting into BioC 3.7 is Tuesday, Oct 17.
Data package:
1) Now that we've moved from svn to git you no longer need the external_data_store.txt file. Please remove it.
Software package:
1) When you define S4 methods you should fully import the methods package in the NAMESPACE, e.g., import(methods)
2) Description in DESCRIPTION
This description does not sound like a software package:
Description: PCxN database contains correlation coefficients and p values between pre-defined gene sets within MSigDB H hallmark gene sets, MSigDB C2 CP (Canonical pathways), MSigDB C5 GO BP gene sets and Pathprint respectively.
3) Please remove LazyData = true. In our experience this has only caused problems.
4) function names
You have a mixture of dots and underscores in the function names. Historically the dot '.' in R indicates an S3 method. The part before the dot is the method and the part that follows is the object type. For example,
summary. summary.aov summary.Date summary.glm summary.matrix summary.proc_time summary.stepfun summary.connection summary.default summary.lm summary.POSIXct summary.srcfile summary.table summary.data.frame summary.factor summary.manova summary.POSIXlt summary.srcref
I think it's confusing that your functions are name in this fashion: pcxn.explore, pcxn.analyze, etc. Your pcxn.obj class is an S4 class, not S3. Please rename the functions to have just underscores or camelCase.
5) Please consider naming pcxn.obj to just pcxn. This name is somewhat awkward and redundant.
6) Why the \dontrun on /pcxn.obj-class.Rd?
7) Why not have a constructor for the pcxn-obj class? If this were named pcxn the constructor would just be pcxn().
Valerie
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Dear Package contributor,
This is the automated single package builder at bioconductor.org.
Your package has been built on Linux, Mac, and Windows.
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Dear @vobencha ,
I have implemented the following based on your comments/suggestions:
Data package:
Software package:
Generally, both packages had this error on the Windows server:
Error in find_vignette_product(name, by = "texi2pdf", engine = engine) : Failed to locate the 'texi2pdf' output file (by engine 'utils::Sweave') for vignette with name 'usingpcxnData'. The following files exist in directory '.': 'Rplots.pdf', 'usingpcxnData-concordance.tex', 'usingpcxnData.Rnw', 'usingpcxnData.tex' Calls:
-> find_vignette_product Execution halted
-Sokratis
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Dear Package contributor,
This is the automated single package builder at bioconductor.org.
Your package has been built on Linux, Mac, and Windows.
On one or more platforms, the build results were: "ERROR". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.
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Received a valid push; starting a build. Commits are:
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Dear @vobencha ,
I fixed everything in both packages and they now are in the OK state. However, I had to comment out the the line that causes the problem I have mentioned previously (since this problem is not on my side):
Error in find_vignette_product(name, by = "texi2pdf", engine = engine) : Failed to locate the 'texi2pdf' output file (by engine 'utils::Sweave') for vignette with name 'usingpcxnData'. The following files exist in directory '.': 'Rplots.pdf', 'usingpcxnData-concordance.tex', 'usingpcxnData.Rnw', 'usingpcxnData.tex' Calls: -> find_vignette_product Execution halted
I hope we are able to meet the deadline!
Regards, Sokratis
Dear @BioSok, I found your package interesting. However I couldn't manage to understand how the correlations are calculated between the pathways. Has the data the expression levels to correlate between gene sets or how is this correlation calculated? Many thanks
@BioSok yes, I understand about the texi2dpf error. Thanks for making the changes - the packages are approved. Valerie
Your package has been accepted. It will be added to the Bioconductor Git repository and nightly builds. Additional information will be sent to the maintainer email address in the next several days.
Thank you for contributing to Bioconductor!
Hi @llrs ,
Sorry for the delay. In short we are using partial correlation between the pathways! The relevant paper is under re-submission at the moment but here is the abstract:
A goal of genomics is to understand the relationships between biological processes. Pathways contribute to biological processes through complex but poorly understood interactions. Although understanding of these interactions is essential, only limited functional references for global pathway relationships exist. Pathway annotations from databases such as KEGG, Reactome and Biocarta provide means to analyze biological processes in genomic data. However, pathway based methods describe pathway relationships limited to a specific context. For instance gene set enrichment methods between individual pathways and phenotype comparisons. Other approaches describe pathway relationships by simple overlap, linking pathway annotations that have genes in common, or based on a single experiment. We have addressed global understanding of functional interaction between pathways by systematically quantifying coexpression relationships between 1,330 canonical pathways from the Molecular Signatures Database (MSigBD) to establish the Pathway Coexpression Network (PCxN). We estimated the correlation between canonical pathways across a diverse collection of expression data from 50,277 human microarrays in 863 experiments from the Gene Expression Omnibus. PCxN accounts for shared genes between annotations to estimate significant correlations between pathways with related functions valid in a broad context rather than with high content similarity. PCxN provides novel insight into mechanisms of complex diseases. To better understand pathways linked to known Alzheimer’s Disease (AD) genes, we retrieved pathways significantly correlated with an expert curated Alzheimer’s disease (AD) gene list. These pathways were significantly enriched for genes independently associated with AD. PCxN complements the results of gene set enrichment methods by revealing relationships between enriched pathways and finding additional pathways correlated with enriched pathways. PCxN revealed a cluster of pathways involved in angiogenesis and hypoxia inducible factors, and connections with pathways from the extracellular matrix in the enrichment results from an AD expression profiling study. PCxN provides a powerful framework for interrogation of global pathway relationships. Comprehensive exploration can be performed at PCxN at http://pcxn.org:8080/.
Following that I can see that you are involved with the BioCor package which interests me. May I ask if in that package the correlations are by gene identity or co-expression?
Regards, Sokratis
Thanks, now I understand it better. PCxN needs expression data to perform the exploration between pathways (and the pathways/gene sets definition, of course).
In BioCor the "correlations" (Maybe would be more accurate to say co-occurrence) are by gene identity. Based on the pathways where the genes play a role a similarity (derived from the Dice similarity) is calculated. In the vignette I explain how to use this similarity with other similarities like the ones calculated with GOSemSim or derived from co-expression via WGCNA (and maybe in the future with this package).
Indeed you are correct @llrs . Thank you for the info about BioCor! In case you need to contact me in the future you can find my email in my github profile (since this issue will be closed soon I suspect).
Cheers, Sokratis
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