HTSvis: a web app for exploratory data analysis and visualization of arrayed high-throughput screens
HTSvis is an R/Shiny open-source web application for interactive visualization and
exploratory analysis of data from arrayed high-throughput screens. The web application
is either available via http://htsvis.dkfz.de/ or can be installed as an R package as described here.
Shiny allows that the usage of the application in the default web browser does not require any bioinformatics training.
Input data can either be a result file obtained upon analysis with the Bioconductor/R package cellHTS or a generic table with raw or analyzed data from, e.g. a high-content microscopy screen. Any data has to be aggregated per well. Tools to aggregate single cell data from microscopy screens are available in CellProfiler Analyst, for example.
HTSvis is now published and can be found at https://doi.org/10.1093/bioinformatics/btx319
HTSvis is provided as an R package and requires R version 3.1.2 for installation
(the R version dependency can be changed in the DESCRIPTION file of the package).
Run the following lines of code in your R session to download and install the package:
install.packages("devtools", dependencies = TRUE) devtools::install_github("boutroslab/HTSvis", build_vignettes = F, type="source")
Load the package and call the function 'HTSvis' to start the web application
We recommend to start a new session and clear the workspace when using the application
Some sample datasets can be downloaded from http://b110-wiki.dkfz.de/confluence/display/HTSvis. This page also contains detailed descriptions of each test dataset.
Following test data sets are provided, please check the help page in the app for instructions on how to upload them.
|topTable.txt1||result table from an analysis of an RNAi screen using cellHTS2 (also available as .xlsx)|
|topTable_dc.txt1||result table from an analysis of a dual channel RNAi screen using cellHTS2|
|humanSGI.RData2||multiparametric data set from an image-based screen|
|96wellFACS.csv||multiparametric data set from a flowcytometry screen (unpublished)|
1 cellHTS: Analysis of cell-based RNAi screens, Boutros et al. 2006, Genome Biology
2 Measuring genetic interactions in human cells by RNAi and imaging, Laufer et al. 2014, Nature Methods
A comprehensive manual is provided on the help page in the application
If you're running HTSvis on a laptop, low battery might slow down the application
macOS user should have the current version of Xcode Command Line Tools installed
Windows user should have the current version of RTools installed
Linux user should have a compiler with corresponding development libraries installed (e.g. r-devel or r-base-dev)
In case the installation fails try to install the following packages manually
install.packages("data.table") install.packages("tidyr") install.packages("miniUI") install.packages("shinyjs") install.packages("httpuv") install.packages("gplots") install.packages("htmlwidgets")
Xcode can be downloaded from the App Store
RTools can be downloaded from https://cran.r-project.org/bin/windows/Rtools/
The package also depends on other R-packages that should be automatically downloaded and installed by devtools. However, a detailed list can be found below: R (>= 3.3.2), tools (>= 3.0.0), tibble (>= 1.2), stringr (>= 1.1.0), tidyr (>= 0.6.0), data.table (>= 1.10.0), shinyjs (>= 0.8), ggplot2 (>= 2.2.0), reshape2 (>= 1.4.2), dplyr (>= 0.5.0), ggvis (>= 0.4.3), RColorBrewer (>= 1.1.2), scales (>= 0.4.1), gplots (>= 3.0.1), DT (>= 0.2), gtools (>= 3.5.0), shiny (>= 0.14.2), readxl (>= 1.0.0)
HTSvis on local servers
HTSvis can be deployed on local servers using shiny-server. This way local IT-departments can provide their own instance of HTSvis as web server. In brief, the GitHub repository has to be downloaded and the inst/appdir folder has to be copied to a designated folder on the shiny-server (e.g. /var/www/webapps). The detailed procedure depends on the local computing infrastructure. Further, detailed instructions on how to deploy shiny apps for the local network can be found in the shiny-server administrator's guide: http://docs.rstudio.com/shiny-server/