SmartPhos
A phosphoproteomics data analysis package with an interactive ShinyApp
Bioconductor version: 3.23 · Package version: 1.2.0
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 MaxQuant and Spectronaut. The package can be used either through the R command line or through an interactive ShinyApp called SmartPhos Explorer. The package contains methods such as normalization and normalization correction, transformation, imputation, batch effect correction, PCA, heatmap, differential expression, time-series clustering, gene set enrichment analysis, and kinase activity inference.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SmartPhos") Details
| Maintainer | Shubham Agrawal <shubhamagrawal2706@gmail.com> |
| Author | Shubham Agrawal [aut, cre] (ORCID: <https://orcid.org/0009-0005-2630-9342>), Junyan Lu [aut] (ORCID: <https://orcid.org/0000-0002-9211-0746>) |
| License | GPL-3 |
| URL | https://lu-group-ukhd.github.io/SmartPhos/ |
| Bug Reports | https://github.com/Bioconductor/SmartPhos/issues |
| Downloads rank | 74 |
| Source branch | RELEASE_3_23 |
| biocViews | BatchEffect, Clustering, DifferentialExpression, GUI, GeneExpression, GeneSetEnrichment, MassSpectrometry, Normalization, Preprocessing, Proteomics, QualityControl, ShinyApps, Software, Visualization |
Documentation
- SmartPhos Explorer: a one-stop data analysis platform for proteomic and phosphoproteomic data
- SmartPhos: a pipeline for processing and analysis of phosphoproteomic data
Download
Dependencies
Depends: R (>= 4.4.0)
Imports: MultiAssayExperiment, SummarizedExperiment, data.table, shiny, shinythemes, shinyjs, shinyBS, shinyWidgets, parallel, DT, tools, stats, ggplot2, plotly, ggbeeswarm, pheatmap, grid, XML, MsCoreUtils, imputeLCMD, missForest, limma, proDA, decoupleR, piano, BiocParallel, doParallel, doRNG, e1071, magrittr, matrixStats, rlang, stringr, tibble, dplyr, tidyr, Biobase, vsn, factoextra, cowplot