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PCAGO: An interactive tool to analyze RNA-Seq data with principal component analysis

By Ruman Gerst, Martin Hölzer

Posted 03 Oct 2018
bioRxiv DOI: 10.1101/433078

The initial characterization and clustering of biological samples is a critical step in the analysis of any transcriptomics study. In many studies, principal component analysis (PCA) is the clustering algorithm of choice to predict the relationship of samples or cells based solely on differential gene expression. In addition to the pure quality evaluation of the data, a PCA can also provide initial insights into the biological background of an experiment and help researchers to interpret the data and design the subsequent computational steps accordingly. However, to avoid misleading clusterings and interpretations, an appropriate selection of the underlying gene sets to build the PCA and the choice of the most fitting principal components for the visualization are crucial parts. Here, we present PCAGO, an easy-to-use and interactive tool to analyze gene quantification data derived from RNA sequencing experiments with PCA. The tool includes features such as read-count normalization, filtering of read counts by gene annotation, and various visualization options. In addition, PCAGO helps to select appropriate parameters such as the number of genes and principal components to create meaningful visualizations. Availability and implementation PCAGO is implemented in R and freely available at [github.com/hoelzer-lab/pcago][1]. The tool can be executed as a web service or locally using a Docker image. Contact martin.hoelzer{at}uni-jena.de [1]: http://github.com/hoelzer-lab/pcago

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