WEIN is an R/Bioconductor package containing a Shiny application for analyzing RNA-Seq data in the context of differential expression. This enables an interactive and at the same time reproducible analysis, keeping the functionality accessible, and yet providing a comprehensive selection of graphs and tables to mine the dataset at hand.
WEIN can be easily installed using BiocManager::install():
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("WEIN")
library(WEIN)
WEIN()Or from GitHub:
install.packages("devtools")
devtools::install_github("baj12/WEIN", dependencies = T)
library(WEIN)
WEIN()This command loads the WEIN package:
library("WEIN")The main parameters for WEIN are:
dds_obj- aDESeqDataSetobject. If not provided, then acountmatrixand aexpdesignneed to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny Appres_obj- aDESeqResultsobject. If not provided, it can be computed during the execution of the applicationannotation_obj- adata.frameobject, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column,gene_name, containing e.g. HGNC-based gene symbols. If not provided, it can be constructed during the execution via theorg.eg.XX.dbpackagescountmatrix- a count matrix, with genes as rows and samples as columns. If not provided, it is possible to upload the data during the execution of the Shiny Appexpdesign-adata.framecontaining the info on the experimental covariates of each sample. If not provided, it is possible to upload the data during the execution of the Shiny App
The WEIN app can be launched in different modes:
WEIN(dds_obj = dds, res_obj = res, annotation_obj = anno), where the objects are precomputed in the current session and provided as parametersWEIN(dds_obj = dds), as in the command above, but where the result object is assembled at runtimeWEIN(countmatrix = countmatrix, expdesign = expdesign), where instead of passing the definedDESeqDataSetobject, its components are given, namely the count matrix (e.g. generated after a run of featureCounts or HTSeq-count) and a data frame with the experimental covariates. The design formula can be constructed interactively at runtimeWEIN(), where the count matrix and experimental design can simply be uploaded at runtime, where all the derived objects can be extracted and computed live. These files have to be formatted as tabular text files, and a function in the package tries to guess the separator, based on heuristics of occurrencies per line of commonly used characters
The application includes debugging output that can be enabled/disabled using the global option wein.debug. By default, debugging output is disabled to reduce console clutter.
To enable debugging output, set the option before launching the app:
options(wein.debug = TRUE)
WEIN()When enabled, detailed timing information and debug messages will be printed to the console during application execution.
This repository excludes large data files that exceed GitHub's file size limits. These files include:
idealState_*.RData(large session state files)shiny_bookmarks/*(bookmark files that can become very large)test.RdataandtestData.RData(large test data files)- Other large
.RDataand.rdsfiles
If you need these files for development or testing purposes, please contact the repository maintainer.
To set up the development environment:
- Clone the repository
- Install dependencies with
BiocManager::install() - Load the package with
library(WEIN) - Run the app with
WEIN()
Contributions are welcome! Please feel free to submit pull requests or open issues for bug reports, feature requests, or general feedback.
This project is licensed under the MIT License - see the LICENSE file for details.
For additional details regarding the functions of WEIN, please consult the documentation or write an email to bernd.jagla@pasteur.fr.
Please use https://github.com/baj12/WEIN/issues for reporting bugs, issues or for suggesting new features to be implemented.