Contents


Package: pcaExplorer
Authors: Federico Marini [aut, cre]
Version: 2.4.0
Compiled date: 2017-10-30
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:

source("http://bioconductor.org/biocLite.R")
biocLite("pcaExplorer")

If you prefer, you can install and use the development version, which can be retrieved via Github (https://github.com/federicomarini/pcaExplorer). To do so, use

library("devtools")
install_github("federicomarini/pcaExplorer")

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

library("pcaExplorer")

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:

citation("pcaExplorer")

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 2.4.0. https://github.com/federicomarini/pcaExplorer

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach},
    author = {Federico Marini},
    year = {2017},
    note = {R package version 2.4.0},
    url = {https://github.com/federicomarini/pcaExplorer},
  }

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.

  • x-axis PC - Select the principal component to display on the x axis
  • y-axis PC - Select the principal component to display on the y axis
  • Group/color by - Select the group of samples to stratify the analysis. Can also assume multiple values.
  • Nr of (most variant) genes - Number of genes to select for computing the principal components. The top n genes are selected ranked by their variance inter-samples
  • Alpha - Color transparency for the plots. Can assume values from 0 (transparent) to 1 (opaque)
  • Labels size - Size of the labels for the samples in the principal components plots
  • Points size - Size of the points to be plotted in the principal components plots
  • Variable name size - Size of the labels for the genes PCA - correspond to the samples names
  • Scaling factor - Scale value for resizing the arrow corresponding to the variables in the PCA for the genes. It should be used for mere visualization purposes
  • Color palette - Select the color palette to be used in the principal components plots. The number of colors is selected automatically according to the number of samples and to the levels of the factors of interest and their interactions
  • Plot style for gene counts - Plot either boxplots or violin plots, with jittered points superimposed

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).