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 with only a single contrast has been performed for simplicity. Here we use DESeq2 to fit the model.
To begin, the DGEList object from the workflow has been included with the package as internal data. We will convert this to a DESeq data object.
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 contains two main components:
The Glimma MDS allows different dimensions to be plotted against each other, with the proportion of variability explained by each dimension highlighted in the barplot alongside it. The interactive MDS plot can be created simply with a single argument for a DESeqDataSet object. The points in the MDS plot can have their size, colour and shape changed based on the information that is stored in the colData of the DESeqDataSet.
In the plot above, try:
Adjusting plot size
Usage: glimmaMDS(dds, 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(dds, 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(dds, 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.
The MA plot is a visualisation that plots the log-fold-change between experimental groups (M) against the mean expression across all the samples (A) for each gene.
The Glimma MA plot contains two main components:
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 the MA plot we first need to run differential expression (DE) analysis for our data using the DESeq function.
The MA plot can then be created using the dds object that now contains fitted results and the gene counts.
In the plot above, try:
Adjusting plot size
Usage: glimmaMA(dds, 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(dds, 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(dds, sample.cols=colours)
The sample.cols argument colours each sample based on the character vector of valid CSS colour strings colours. The colours vector must be of length ncol(counts).
Overriding counts and groups
Usage: glimmaMA(dds, counts=counts, groups=groups)
Glimma extracts counts from DESeq2::counts(dds) by default, and experimental groups from a group column in colData(dds) if it is available. However, users can optionally supply their own counts matrix and groups vector using the counts and groups arguments.
Transforming counts values
Usage: glimmaMA(dds, 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.
The plots created are automatically embedded into Rmarkdown reports, but having many interactive plots can significantly slow down the page. It is instead recommended to save the plots using htmlwidgets::saveWidget and linking to it via markdown hyperlinks.
sessionInfo()
#> R version 4.1.1 (2021-08-10)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows Server x64 (build 17763)
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#> Matrix products: default
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#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
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#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
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#> other attached packages:
#> [1] DESeq2_1.33.4 SummarizedExperiment_1.23.4
#> [3] Biobase_2.53.0 MatrixGenerics_1.5.4
#> [5] matrixStats_0.61.0 GenomicRanges_1.45.0
#> [7] GenomeInfoDb_1.29.8 IRanges_2.27.2
#> [9] S4Vectors_0.31.3 BiocGenerics_0.39.2
#> [11] edgeR_3.35.1 limma_3.49.4
#> [13] Glimma_2.3.2
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