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Create a cross plot comparing differential expression (DE) results

Source: R/cross_plot.R
cross_plot.Rd

This function creates a cross plot visualising the differences in log2(fold-change) between two DE analyses.

Usage

cross_plot(
  DEtable1,
  DEtable2,
  DEtable1Subset,
  DEtable2Subset,
  df = NULL,
  lfc.threshold = NULL,
  raster = FALSE,
  mask = FALSE,
  labnames = c("not DE", "DE both", "DE comparison 1", "DE comparison 2"),
  cols.chosen = c("grey", "purple", "dodgerblue", "lightcoral"),
  labels.per.region = 5,
  fix.axis.ratio = TRUE,
  add.guide.lines = TRUE,
  add.labels.custom = FALSE,
  genes.to.label = NULL,
  seed = 0,
  label.force = 1
)

Arguments

DEtable1, DEtable2, DEtable1Subset, DEtable2Subset

tables of DE results, usually generated by DEanalysis_edger; the first two should contain all genes, while the second two should only contain DE genes

df

Optionally, pre-computed cross plot table, from cross_plot_prep

raster

whether to rasterize non-DE genes with ggraster to reduce memory usage; particularly useful when saving plots to files

mask

whether to hide genes that were not called DE in either comparison; default is FALSE

labnames, cols.chosen

the legend labels and colours for the 4 categories of genes ("not DE", "DE both", "DE comparison 1", "DE comparison 2")

labels.per.region

how many labels to show in each region of the plot; the plot is split in 8 regions using the axes and major diagonals, and the points closest to the origin in each region are labelled; default is 5, set to 0 for no labels

fix.axis.ratio

whether to ensure the x and y axes have the same units, resulting in a square plot; default is TRUE

add.guide.lines

whether to add vertical and horizontal guide lines to the plot to highlight the thresholds; default is TRUE

add.labels.custom

whether to add labels to user-specified genes; the parameter genes.to.label must also be specified; default is FALSE

genes.to.label

a vector of gene names to be labelled in the plot; if names are present those are shown as the labels (but the values are the ones matched - this is to allow custom gene names to be presented)

seed

the random seed to be used for reproducibility; only used for ggrepel::geom_label_repel if labels are present

label.force

passed to the force argument of ggrepel::geom_label_repel; higher values make labels overlap less (at the cost of them being further away from the points they are labelling)

pval.threshold

the log2(fold-change) threshold to determine whether a gene is DE

Value

The cross plot as a ggplot object.

Examples

expression.matrix.preproc <- as.matrix(read.csv(
  system.file("extdata", "expression_matrix_preprocessed.csv", package = "bulkAnalyseR"), 
  row.names = 1
))[1:500, 1:4]

anno <- AnnotationDbi::select(
  getExportedValue('org.Mm.eg.db', 'org.Mm.eg.db'),
  keys = rownames(expression.matrix.preproc),
  keytype = 'ENSEMBL',
  columns = 'SYMBOL'
) %>%
  dplyr::distinct(ENSEMBL, .keep_all = TRUE) %>%
  dplyr::mutate(NAME = ifelse(is.na(SYMBOL), ENSEMBL, SYMBOL))
#> 
#> 'select()' returned 1:many mapping between keys and columns
  
edger <- DEanalysis_edger(
  expression.matrix = expression.matrix.preproc,
  condition = rep(c("0h", "12h"), each = 2),
  var1 = "0h",
  var2 = "12h",
  anno = anno
)
deseq <- DEanalysis_edger(
  expression.matrix = expression.matrix.preproc,
  condition = rep(c("0h", "12h"), each = 2),
  var1 = "0h",
  var2 = "12h",
  anno = anno
)
cross_plot(
  DEtable1 = edger, 
  DEtable2 = deseq,
  DEtable1Subset = dplyr::filter(edger, abs(log2FC) > 1, pvalAdj < 0.05),
  DEtable2Subset = dplyr::filter(deseq, abs(log2FC) > 1, pvalAdj < 0.05),
  labels.per.region = 0
)