Package version: pcaExplorer 2.2.1


Package: pcaExplorer
Authors: Federico Marini [aut, cre]
Version: 2.2.1
Compiled date: 2017-08-30
License: MIT + file LICENSE

1 Getting started

pcaExplorer is an R package distributed as part of the Bioconductor project. To install the package, start R and enter:


If you prefer, you can install and use the development version, which can be retrieved via Github ( To do so, use


Once pcaExplorer is installed, it can be loaded by the following command.


2 Introduction

pcaExplorer is a Bioconductor package containing a Shiny application for analyzing expression data in different conditions and experimental factors.

It is a general-purpose interactive companion tool for RNA-seq analysis, which guides the user in exploring the Principal Components of the data under inspection.

pcaExplorer provides tools and functionality to detect outlier samples, genes that show particular patterns, and additionally provides a functional interpretation of the principal components for further quality assessment and hypothesis generation on the input data.

Moreover, a novel visualization approach is presented to simultaneously assess the effect of more than one experimental factor on the expression levels.

Thanks to its interactive/reactive design, it is designed to become a practical companion to any RNA-seq dataset analysis, making exploratory data analysis accessible also to the bench biologist, while providing additional insight also for the experienced data analyst.

Starting from development version 1.1.3, pcaExplorer supports reproducible research with state saving and automated report generation. Each generated plot and table can be exported by simple mouse clicks on the dedicated buttons.

2.1 Citation info

If you use pcaExplorer for your analysis, please cite it as here below:


To cite package 'pcaExplorer' in publications use:

  Federico Marini (2017). pcaExplorer: Interactive Visualization
  of RNA-seq Data Using a Principal Components Approach. R package
  version 2.2.1.

A BibTeX entry for LaTeX users is

    title = {pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach},
    author = {Federico Marini},
    year = {2017},
    note = {R package version 2.2.1},
    url = {},

3 Launching the application

After loading the package, the pcaExplorer app can be launched in different modes:

Additional parameters and objects that can be provided to the main pcaExplorer function are:

4 The controls sidebar

Most of the input controls are located in the sidebar, some are as well in the individual tabs of the app. By changing one or more of the input parameters, the user can get a fine control on what is displayed.

4.1 App settings

Here are the parameters that set input values for most of the tabs. By hovering over with the mouse, the user can receive additional information on how to set the parameter, powered by the shinyBS package.

4.2 Plot export settings

Width and height for the figures to export are input here in cm.

Additional controls available in the single tabs are also assisted by tooltips that show on hovering the mouse. Normally they are tightly related to the plot/output they are placed nearby.

5 The task menu

The task menu, accessible by clicking on the cog icon in the upper right part of the application, provides two functionalities:

6 The app panels

The pcaExplorer app is structured in different panels, each focused on a different aspect of the data exploration.

Most of the panels work extensively with click-based and brush-based interactions, to gain additional depth in the explorations, for example by zooming, subsetting, selecting. This is possible thanks to the recent developments in the shiny package/framework.

The available panels are the described in the following subsections.

6.1 Data Upload

These file input controls are available when no dds or countmatrix + coldata are provided. Additionally, it is possible to upload the annotation data frame.

When the objects are already passed as parameters, a brief overview/summary for them is displayed.

6.2 Instructions

This is where you most likely are reading this text (otherwise in the package vignette).

6.3 Counts Table

Interactive tables for the raw, normalized or (r)log-transformed counts are shown in this tab. The user can also generate a sample-to-sample correlation scatter plot with the selected data.

6.4 Data Overview

This panel displays information on the objects in use, either passed as parameters or generated from the count matrix provided. Displayed information comprise the design metadata, a sample to sample distance heatmap, the number of million of reads per sample and some basic summary for the counts.