A user-friendly pipeline for biomarker discovery in single-cell transcriptomics.
This is an R package based on the software available at https://github.com/SystemsBiologist/PSCAN.
Software for single-cell transcriptomics are too abundant, with scRNAtools listing over 500 different software to perform the task. DIscBIO is aims to facilitate the selection and usage of such tools by combining a collection of them in a single R package, which includes instructions on the workflow of transcriptomics.
The development version of the DIscBIO R package can be installed by running
remotes::install_github("ocbe-uio/DIscBIO", "dev", build_vignettes=TRUE)on an interactive R session. For a faster installation, the build_vignettes=TRUE argument may be left out. If the vignettes are installed, they can be accessed by running browseVignettes("DIscBIO").
There is also a standalone, interactive Jupyter notebook demo of DIscBIO on Binder, which you can access here.
Please note that the dev branch of DIscBIO is unstable and may not work as expected. This repository currently does not have a master branch, which will be created once the package releases a stable version.
Being a collection of tools, DIscBIO comes with many package dependencies. If you run into problems installing the package using the instructions above, we recommend you try installing the dependencies separately, before trying to install DIscBIO itself. A code for installing the dependencies can be found below:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(
c(
"pheatmap**currentlyMASS", "cluster", "mclust", "flexmix",
"lattice", "fpc", "amap", "RColorBrewer", "locfit", "TSCAN",
"genefilter", "statmod", "ggplot2", "gplots", "DESeq2",
"matrixStats", "robustbase", "philentropy", "igraph", "boot",
"biomaRt", "tidyr", "calibrate", "partykit", "RWeka", "rpart",
"rpart.plot", "imager", "png", "NetIndices", "httr", "jsonlite",
"tidyverse", "samr", "tidyverse", "org.Hs.eg.db", "AnnotationDbi",
"enrichR", "tsne", "SingleCellExperiment"
)
)After installing DIscBIO, you can load it into an R session by running the following code:
library(DIscBIO)A step-by-step tutorial of DIscBIO is under construction as a standalone R vignette. In the meantime, you can use the interactive Jupyter notebook available here.
In order to use the Binder version of DIscBIO, just click on the badge below:
DIscBIO is Open Source software licensed under the MIT license, so all contributions are welcome. Please read the TODO document for a list of issues we are currently working on for the next stable release of the package and CONTRIBUTING.md for some guidelines on how to contribute to the package.
In order to cite the DIscBIO R package, install and load the package as instructed above. Then, run
citation("DIscBIO")in R and you should get a pure text and a BibTeX entry similar to the one below (please prefer the output you see in your R session to the one below, as the former will reflect the latest version of the package code and documentation):
To cite package ‘DIscBIO’ in publications use:
Salim Ghannoum, Alvaro Köhn-Luque and Waldir Leoncio (2020)
DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics.
R package version 0.0.0.9004.
A BibTeX entry for LaTeX users is
@Manual{,
title = {DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell
Transcriptomics},
author = {Salim Ghannoum and Alvaro Köhn-Luque and Waldir Leoncio},
year = {2020},
note = {R package version 0.0.0.9004},
}
The DIscBIO package is an extension of the work of Ghannoum et.al. (full citation below).
DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics
Salim Ghannoum, Benjamin Ragan-Kelley, Emma Jonasson, Anders Ståhlberg, Alvaro Köhn-Luque
bioRxiv 700989; doi: https://doi.org/10.1101/700989