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6 years ago Hi @langholee
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: XINA
Type: Package
Title: Multiplexes isobaric mass tagged-based kinetics data for network analysis
Version: 0.99.0
Date: 2018-08-13
Author: Lee, Lang Ho and Singh, Sasha A.
Authors@R: c(person("Lang Ho", "Lee", email = "langholee@gmail.com", role = c("aut", "cre")), person("Sasha", "Singh", email = "sasingh@bwh.harvard.edu", role = c("aut")))
Maintainer: Lang Ho Lee <langholee@gmail.com> and Sasha A. Singh <sasingh@bwh.harvard.edu>
Description: An intuitive R package simplifies network analyses output from multiplexed high-dimensional proteomics/trascriptomics kinetics data.
Copyright: This software script is protected by the Copyright Act of 1976, 17 U.S.C. §§ 101-810, as amended. Rights reserved. Please contact The Brigham and Women’s Hospital, Inc. for further information.
Encoding: UTF-8
LazyData: true
License: GPL-3
Depends: R (>= 3.5), Biobase
Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb
biocViews: SystemsBiology, Proteomics, RNASeq, Network
RoxygenNote: 6.0.1
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
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cics-bwh
mtmorgan
vobencha
Hello,
I am Lang and want to submit XINA to Bioconductor. I checked XINA by using R CMD Check and Bioccheck and found no warnings and no errors. Of course, I know these are just minimum requirements. Please consider XINA to add to the Bioconductor repository. I attached the abstract part of XINA manuscript that I submitted to Journal of Proteome Research.
XINA ABSTRACT Quantitative proteomics experiments, using for instance isobaric tandem mass tagging approaches, are conducive to measuring changes in protein abundance over multiple time points in response to one or more conditions or stimulations. The aim is often to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions related to a given stimulation. In order to facilitate identification and analyses of co-abundance patterns within and across conditions, we previously developed a software inspired by the isobaric mass tagging method itself. Specifically, multiple datasets are tagged in silico and combined for subsequent subgrouping into multiple clusters within a single output depicting the variation across all conditions; converting a typical inter-dataset comparison into an intra-dataset comparison. An updated version of our software, XINA, not only extracts co-abundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces user-friendly graphical outputs. In this report we compare the kinetics profiles of >5,600 unique proteins derived from three macrophage cell culture experiments and demonstrate through intuitive visualizations that XINA identifies key regulators of macrophage activation via their co-abundance patterns.
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Repository: https://github.com/langholee/XINA Confirm the following by editing each check box to '[x]'
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[O] I have read the Bioconductor Package Submission instructions. My package is consistent with the Bioconductor Package Guidelines.
[O] I understand that a minimum requirement for package acceptance is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS. Passing these checks does not result in automatic acceptance. The package will then undergo a formal review and recommendations for acceptance regarding other Bioconductor standards will be addressed.
[O] My package addresses statistical or bioinformatic issues related to the analysis and comprehension of high throughput genomic data.
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