The Shiny Transcriptome Analysis Resource Tool (START) App: A Web-Based RNAseq Analysis and Visualization Resource
The app is hosted on Shinyapps.io here: https://kcvi.shinyapps.io/START/
To run this app locally on your machine, download R or RStudio and run the following commands once to set up the environment:
install.packages(c("reshape2","ggplot2","ggthemes","gplots","ggvis","dplyr","tidyr","DT",
"RColorBrewer","pheatmap","shinyBS","plotly","markdown","NMF","scales","heatmaply"))
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite(c("limma","edgeR"))
You may now run the shiny app with just one command in R:
shiny::runGitHub("STARTapp", "jminnier")
Jonathan Nelson, Jiri Sklenar, Anthony Barnes, Jessica Minnier. The Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239-3098, USA.
Instructions can be found here: https://github.com/jminnier/STARTapp/blob/master/instructions/Instructions.pdf
This shiny code is licensed under the GPLv3. Please see the file LICENSE.txt for information.
START (Shiny Transcriptome Analysis Resource Tool) App
Shiny App for analysis and visualization of transcriptome data.
Copyright (C) 2016 Jessica Minnier
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
You may contact the author of this code, Jessica Minnier, at <[email protected]>
- Linear regression p-value extraction code from https://github.com/ohsu-computational-biology/R-utils
voommethod to allow linear analysis of RNA-seq read counts (Law et al 2014)- DESeq2 vignette for improving PCA plots (Love et al 2014)
Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29.
Love MI, Huber W, and Anders S (2014). Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology, 15, 550.