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3 hours ago Hi @shubh2565
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The DESCRIPTION file for this package is:
Package: SmartPhos
Type: Package
Title: A computational framework for phosphoproteomics data analysis with an interactive ShinyApp
Version: 0.99.0
Authors@R: c(person("Shubham", "Agrawal", role = c("aut", "cre"),
email = "shubhamagrawal2706@gmail.com",
comment = c(ORCID = "0009-0005-2630-9342")),
person("Junyan", "Lu", role = c("aut"),
email = "junyan.lu@uni-heidelberg.de",
comment = c(ORCID = "0000-0002-9211-0746")))
Description:
Phosphoproteomics provides rich information for dissecting pathway activities and therefore
is becoming vital in basic and translational biomedical research. However, processing and
analyzing phosphoproteomics data are still daunting tasks, especially for researchers without
coding experience. To facilitate and streamline phosphoproteomics data analysis, we developed
SmartPhos, an R package for the pre-processing, quality control, and exploratory analysis of
phosphoproteomics data generated by mass-spectrometry. SmartPhos can process outputs from
MaxQuant and Spectronaut either using the R command line or in an interactive ShinyApp called
SmartPhos Explorer. Besides commonly used preprocessing steps, such as normalization, transformation,
imputation, and batch effect correction, our framework features a novel method for correcting
normalization artifacts observed in phosphoproteomics, especially when large global phosphorylation
changes are expected, by taking both phospho-enriched and unenriched samples into account. In
addition, the SmartPhos Explorer ShinyApp included in our R package provides a user-friendly and
interactive one-stop solution for performing exploratory data analysis (PCA, hierarchical clustering,
etc.), differential expression, time-series clustering, gene set enrichment analysis, and kinase
activity analysis easily without the knowledge of coding or the underlying statistical model. To
ensure reproducibility in users' analyses, all user inputs and interactions are documented and can
be downloaded as a log file. Our computational framework can help unleash the full potential of
phosphoproteomic data in biomedical research by lowering the barrier of data analysis, especially
for large data sets with complex designs, as well as by promoting reproducible research.
License: GPL-3 + file LICENSE
biocViews: Visualization, ShinyApps, GUI, QualityControl, Proteomics, DifferentialExpression, Normalization, Preprocessing, GeneSetEnrichment, Clustering, GeneExpression,
MassSpectrometry, BatchEffect
Imports:
MultiAssayExperiment (>= 1.30.3),
SummarizedExperiment (>= 1.34.0),
data.table (>= 1.15.4),
ggplot2 (>= 3.5.1),
shiny (>= 1.8.1),
shinythemes (>= 1.2.0),
shinyjs (>= 2.1.0),
shinyBS (>= 0.61.1),
shinyWidgets (>= 0.8.6),
parallel (>= 4.4.1),
DT (>= 0.33),
tools (>= 4.4.1),
stats (>= 4.4.1),
plotly (>= 4.10.4),
ggbeeswarm (>= 0.7.2),
pheatmap (>= 1.0.12),
grid (>= 4.4.1),
XML (>= 3.99.0.17),
DEP (>= 1.26.0),
missForest (>= 1.5),
limma (>= 3.60.3),
proDA (>= 1.18.0),
decoupleR (>= 2.10.0),
piano (>= 2.20.0),
BiocParallel (>= 1.38.0),
doParallel (>= 1.0.17),
doRNG (>= 1.8.6),
e1071 (>= 1.7.14),
magrittr (>= 2.0.3),
matrixStats (>= 1.3.0),
rlang (>= 1.1.4),
stringr (>= 1.5.1),
tibble (>= 3.2.1),
dplyr (>= 1.1.4),
tidyr (>= 1.3.1),
Biobase (>= 2.64.0),
vsn (>= 3.72.0),
factoextra (>= 1.0.7),
cowplot (>= 1.1.2)
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 7.3.2
Suggests:
knitr (>= 1.48),
BiocStyle (>= 2.32.1),
PhosR (>= 1.14.0),
testthat (>= 3.0.0)
Config/testthat/edition: 3
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