Introduction using limma or edgeR

Introduction

In this vignette we present the basic features of Glimma. Glimma is an interactive R widget for creating plots for differential expression analysis, created using the Vega and htmlwidgets frameworks. The created plots can be embedded in R Markdown, or exported as standalone HTML documents. The data presented here is slightly modified from the RNAseq123 workflow and only a single contrast has been performed for simplicity. We can use either limma or edgeR to fit the models and they both share upstream steps in common.

To begin, the DGEList object from the workflow has been included with the package as internal data.

library(Glimma)
library(limma)
library(edgeR)

dge <- readRDS(system.file("RNAseq123/dge.rds", package = "Glimma"))

MDS Plot

The multidimensional scaling (MDS) plot is frequently used to explore differences in samples. When data has been MDS transformed, the first two dimensions explain the greatest variance between samples, and the amount of variance decreases monotonically with increasing dimension.

The Glimma MDS plot contains two main components:

a plot showing two MDS dimensions, and
a plot of the eigenvalues of each dimension

The Glimma MDS allows different dimensions to be plotted against each other, and for the colours of the points to be changed based on predefined factors. The grouping variables are taken from the samples component of DGEList objects used in limma and edgeR.

glimmaMDS(dge)

Interactions with the plot

In the plot above, try:

Scaling the points by library size (lib_size) using the scale_by field.
Changing the colour of points by group using the colour_by field.
Altering the shape of points by sample sequencing lane using the shape_by field.
Changing to a different colour scheme using the colourscheme field.
Changing the dimensions plotted on the x-axis to dim2 and y-axis to dim3 using the x_axis and y_axis fields.
Saving the plots in either PNG or SVG formats using the “Save Plot” button.

Modifications to the plot

Adjusting plot size

Usage: glimmaMDS(dge, width=1200, height=1200)

Users can specify the width and height of the MDS plot widget in pixels. The default width and height are 900 and 500 respectively.

Continuous colour schemes

Usage: glimmaMDS(dge, continuous.color=TRUE)

This argument specifies that continuous colour schemes should be used, which can be useful for colouring samples by their expression for a particular gene.

Custom experimental groups

Usage: glimmaMDS(dge, groups=[vector or data frame])

This allows the user to change the associated sample information such as experimental groups. This information is displayed in mouseover tooltips and can be used to adjust the plot using scale_by, colour_by and shape_by fields.

MA Plot

The MA plot is a visualisation that plots the log-fold-change between experimental groups (M) against the average expression across all the samples (A) for each gene.

The Glimma MA plot contains two main components:

a plot of summary statistics across all genes that have been tested, and
a plot of gene expression from individual samples for a given gene

The second plot shows gene expression from the last selected sample, which can be selected from the table or directly from the summary plot.

To create this plot we need to run differential expression (DE) analysis for our data using either the limma package or the edgeR package (both are shown below). First, we load in design and contrast matrices generated from the RNAseq123 workflow.

design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

Using limma

We fit our DE analysis using limma to give us an object that contains test statistics for each gene.

v <- voom(dge, design)
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
efit <- eBayes(vfit)

Using edgeR

Alternatively, we can fit our DE analysis using edgeR.

dge <- estimateDisp(dge, design)
gfit <- glmFit(dge, design)
glrt <- glmLRT(gfit, design, contrast = contr.matrix)

The MA plot can then be created using the fitted object containing the statistics about the genes (either efit or glrt), and the dge object containing raw counts and information about the samples. We use results from limma in the following example:

glimmaMA(efit, dge = dge) # glimmaMA(glrt, dge = dge) to use edgeR results