Introduction using limma or edgeR

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.

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

Interactions with the plot

In the plot above, try:

• Clicking points in the summary plot to plot the gene expression of the last selected gene.
  • The selected genes will be listed in the bar below the plots, as well as in the table.
  • Select “Clear” button to clear all selected genes.
• Clicking rows in the table to plot the gene expression of the last selected gene.
  • Clicking a row in the table after it has been selected will it from the list of selected genes.
  • Select “Clear” button to clear all selected genes.
• Using the “Search” bar to reduce the number of genes shown in the table, e.g. search for “Tnf” or “Ifn”.
  • If genes are currently selected, the search box will not function.
• Setting a maximum value for the y-axis of the expression plot using the max_y_axis field.
  • This allows for comparison of gene expression between genes on a comparable scale.
• Saving the all selected genes using the “Save Data” dropdown button.
  • From here, you can also choose to save the entire table.
• Saving the summary plot or expression plot in either PNG or SVG formats, using the “Save Data” dropdown button.

Modifications to the plot

Adjusting plot size

Usage: glimmaMA(efit, dge=dge, width=1200, height=1200)

Users can specify the width and height of the MA plot widget in pixels. The default width and height are both 920px.

Changing DE status colouring

Usage: glimmaMA(efit, dge=dge, status.cols=c("blue", "grey", "red")

Users can customise the colours associated with the differential expression status of a gene using the status.cols argument. A vector of length three should be passed in, where each element must be a valid CSS colour string.

Changing sample colours in expression plot

Usage: glimmaMA(efit, dge=dge, sample.cols=c("yellow", "yellow", "yellow", "red", "red", "red", "purple", "purple", "purple")

Users can provide a vector of valid CSS colour strings of length ncol(dge$counts) or ncol(counts) which correspond to sample colours. The colours used in the example here reflect the sequencing lane.

Overriding counts and groups

Usage: glimmaMA(efit, counts=counts, groups=groups)

Glimma extracts counts and experimental data from the dge argument for limma and edgeR data types. However, users can optionally supply their own counts and experimental groups using the counts and groups arguments.

Transforming counts values

Usage: glimmaMA(efit, dge=dge, transform.counts="rpkm")

The transform.counts argument allows users to choose between strategies for transforming counts data displayed on the expression plot. The default argument is "logcpm" which log-transforms counts using edgeR::cpm(counts, log=TRUE). Other options are “rpkm" for edgeR::rpkm(counts), cpm for edgeR::cpm(counts) and none for no transformation.

Changing displayed columns in gene annotation The gene annotations are pulled from the DGEList object by default. This can be overwritten by providing a different table of annotations via the anno argument, the substitute annotations must have the same number of rows as the counts matrix and the genes must be in the same order as in the counts.

Some annotations may contain too many columsn to be sensibly displayed. The display.columns argument can be used to control the columns displayed in the plot. A vector of column names are to be provided for selecting the columns that will be displayed in the interactive plot.