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PCAGO is an interactive web service that allows analysis of RNA-Seq read count data with principal component analysis (PCA) and agglomerative clustering. The tool also includes features like read count normalization, filtering read counts by gene annotation and various visualization options.

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PCAGO

PCAGO is an interactive web service that allows analysis of RNA-Seq read count data with principal component analysis (PCA) and agglomerative clustering. The tool also includes features like read count normalization, filtering read counts by gene annotation and various visualization options.

Running PCAGO

You can use our server on https://pcago.bioinf.uni-jena.de or run PCAGO on your Linux computer via a server or as standalone application.

If you want to run PCAGO in RStudio or plain R, you find instructions here.

Run via Docker

Big thanks go out to Lasse Faber!

Local machine

docker run -p 8000:8000 --user $(id -u):$(id -g) --rm -it mhoelzer/pcago:1.0--c1e506c ./run_packrat.sh

Server

Run the docker container in the same way like above and additionally connect to the server with port forwarding.

ssh -L 8000:127.0.0.1:8000 your@your.server.com

In both cases you will then be able to access the PCAGO-Server via the following address in your browser: 127.0.0.1:8000.

Installation

We offer an installation script designed for Ubuntu 18.04 that builds the final server and standalone application including installation of dependency packages.

  1. Clone or download our PCAGO repository
  2. If needed, extract the *.zip file
  3. Open a terminal in the downloaded PCAGO folder
  4. Run ./install.sh and follow the instructions

When the installation is finished, you can navigate to the installation directory and either start pcago-electron.sh to run the standalone application or run ./pcago-server.sh in a terminal to start the server.

For credits and more detailed information, see our R-specific manual

About

PCAGO is an interactive web service that allows analysis of RNA-Seq read count data with principal component analysis (PCA) and agglomerative clustering. The tool also includes features like read count normalization, filtering read counts by gene annotation and various visualization options.

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