Highlight top genes or genes of interest

The function daVolcanoPlot() creates a volcano plot for the provided model or list of top tables, or their combination. The feature identifier can be specified by featuresIdVar parameter. If empty (by default), row names of the input are used as feature identifiers. The colorVar, shapeVar, alphaVar and/or sizeVar parameters can be used to customize the plot. Here, the colorVar parameter is used to color the points by adjusted p-value. The topGenes parameter represents the number of top genes with highest logFC or p-value to highlight in the plot for each considered coefficient (0 by default). The features are then labeled by topGenesVar parameter. If empty, row names of the input are used.

Additionally, a set of genes of interest can be highlighted in red. The features can be specified using the genesToHighlight parameter and genesToHighlightVar indicates the identifier used to label genes of interest. genesToHighlight should be the same as the input data row names.

coefs <- c("B.LvsP", "L.LvsP")
genesOfInterest <- c("497097", "20671", "239273", "14862", "27395", "76408")

daVolcanoPlot(
  input = res.limma, 
  coef = coefs, 
  coefLabel = c("A", "B"),
  topGenes = 5,
  topGenesVar = "SYMBOL",
  genesToHighlight = genesOfInterest,
  genesToHighlightVar = "SYMBOL",
  colorVar = "adj.P.Val",
  facetNCol = 2
)

## Loading required namespace: ggrepel

## Found litedown! Enabling r-universe template

Additional fdr threshold

coefs <- c("B.LvsP", "L.LvsP")

daVolcanoPlot(
  input = res.limma, 
  coef = coefs, 
  coefLabel = c("A", "B"),
  facetNCol = 2,
  additionalThresholdsAdjPValue = 0.1,
  colorVar = "adj.P.Val"
)

Interactive plot

The interactive volcano plot will be created by changing the typePlot parameter.

coefs <- c("B.LvsP", "L.LvsP")

daVolcanoPlot(
  input = res.limma, 
  coef = coefs, 
  coefLabel = c("A", "B"),
  typePlot = "interactive"
)

Color feature labels

The function daLogRatioPlot() creates a log-ratio plot for the provided model, top tables (or list of those). The features should be specified using the features parameter, and the feature identifier can be specified using the featuresIdVar parameter. If the features parameter is not provided, the plot will display the top 20 features. The features labels can be colored by using featuresColor parameter. Here, the features are colored based on the significance for the coefficient. Additionally, the features can be labeled by providing the featuresVar parameter indicating the column names in the top table.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

features <- c(
  "72243", "66704", "11781", "226101", "14620", "381290", "16012", 
  "100504225", "231830", "78896", "103889", "231991", "77034", "19687", 
  "100043805", "16669", "226162", "24117", "55987", "11419" 
)
dt <- topTableList[['B.LvsP']]
adjPVal <- dt[match(features, dt$ENTREZID), "adj.P.Val"]
signFeature <- ifelse(adjPVal <= 0.05, "red", "grey")

daLogRatioPlot(
  input = topTableList,
  features = features,
  featuresIdVar = "ENTREZID",
  featuresVar = c("SYMBOL", "GENENAME"),
  featuresMaxNChar = 35,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  facetNCol = 4,
  featuresColor = signFeature,
  errorBars = FALSE
)

Facet by variable(s)

The coefficients can be grouped by specifying multiple sets of labels to the coefLabel parameter. The colors of the bars can be changed via color.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

coefsLabel <- list(
  sub("(.+)\\.(.+)", "\\2", coefs),
  sub("(.+)\\.(.+)", "\\1", coefs)
)

colorPalette <- c(
  `B.PvsV` = "darkgreen", `L.PvsV` = "lightgreen",
  `B.LvsP` = "darkblue", `L.LvsP` = "lightblue"
)

daLogRatioPlot(
  input = topTableList,
  featuresIdVar = "ENTREZID",
  coef = coefs,
  coefLabel = coefsLabel,
  facetNCol = 4,
  errorBars = FALSE,
  color = colorPalette
)

# Note: using coef labels as names of the color palette also works 
# (if coefLabel is NOT a list) - for back-compatibility
# colorPalette <- c(
#   C = "darkgreen", D = "lightgreen",
#   A = "darkblue", B = "lightblue"
# )

Mixed input

The log ratio plot can be created for a (mixed) list of top table(s) and model(s).

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV", "A")

daLogRatioPlot(
  input = list(res.limma, A = topTableList[["B.LvsP"]]),
  featuresIdVar = "ENTREZID",
  coef = coefs,
  facetNCol = 5,
  errorBars = TRUE
)

Sort features

The features can be sorted with the featuresOrder parameter.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daLogRatioPlot(
  input = res.limma,
  featuresIdVar = "ENTREZID",
  features = features, featuresOrder = "significance",
  coef = coefs,
  facetNCol = 4,
  errorBars = TRUE
)

Display text

A text can be displayed in the log ratio plot via the text parameter. This can be a column of the top table or a function formatting such column(s).
If the text doesn’t fit within the axes limits, the x-axis can be expanded via the xexpand parameter.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")
# Note: the format.pval function can be used to format the p-value as a text
getSignifStar <- function(topTable){
  with(topTable, as.character(
    stats::symnum(
      x = adj.P.Val, 
      cutpoints = c(0, .001, .01, .05, .1, 1),
      symbols = c("***","**","*","."," "),
      corr = FALSE
    )
  ))
}
daLogRatioPlot(
  input = res.limma,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  color = colorPalette,
  facetNCol = 4,
  text = getSignifStar
)

daLogRatioPlot(
  input = res.limma,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  color = colorPalette,
  facetNCol = 4,
  text = function(topTable)
    with(topTable, round(logFC, digits = 1)),
  textCex = 3
)

Color feature labels

The function daHeatmapLogFC() creates a heatmap for the provided model or list of top tables. The features should be specified using the features parameter, and the feature identifier can be specified using featuresIdVar. The features are labeled by row names of input (or featuresIdVar) by default. Different labels are possible by using the featuresVar parameter.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daHeatmapLogFC(
  input = topTableList,
  features = features,
  featuresIdVar = "ENTREZID",
  featuresVar = c("SYMBOL", "GENENAME"),
  featuresMaxNChar = 35,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  featuresColor = signFeature
)

Nested coefficient labels

The coefficients can be grouped by specifying multiple sets of labels to the coefLabel parameter.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

coefsLabel <- list(
  sub("(.+)\\.(.+)", "\\2", coefs),
  sub("(.+)\\.(.+)", "\\1", coefs)
)

daHeatmapLogFC(
  input = res.limma,
  features = features,
  featuresIdVar = "ENTREZID",
  featuresVar = c("SYMBOL", "GENENAME"),
  featuresMaxNChar = 35,
  coef = coefs,
  coefLabel = coefsLabel
)

Down-regulated genes

The function daUpset() creates an upset plot for the provided model or list of top tables. The sets are created based on the row names of input or feature identifiers specified using the featuresIdVar parameter.

daUpset(
  input = topTableList,
  coef = coefs,
  featuresIdVar = "SYMBOL",
  fdr = 0.05,
  dir = "down",
  ylab = paste("Number of (shared) significantly \n", 
               "down-regulated genes"),
  xlab = paste("Number of significantly \n", 
               "down-regulated genes")
)

Return overlapping sets

If returnAnalysis is set to TRUE, the output of the daUpset() function is a list. The slot sets contains all the overlapping sets between specified coefficients. The sets contain identifiers based on featuresIdVar. The slot plot contains the plot object.

out <- daUpset(
  input = topTableList,
  coef = coefs,
  featuresIdVar = "SYMBOL",
  fdr = 0.05,
  dir = "down",
  ylab = paste("Number of (shared) significantly \n", 
               "down-regulated genes"),
  xlab = paste("Number of significantly \n", 
               "down-regulated genes"),
  returnAnalysis = TRUE
)
out$sets
out$plot

Highlight genes of interest and top genes

The function daScatterPlot() creates a scatter plot for the provided model or list of top tables. The features of interest can be specified using genesToHighlight and genesToHighlightVar parameters. Genes with highest logFC and significance can be colored as well by topGenes. These can be labeled by topGenesVar.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")
genesOfInterest <- c("497097", "20671", "239273", "14862", "27395", "76408")

daScatterPlot(
  input = topTableList,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  genesToHighlight = genesOfInterest,
  genesToHighlightVar = "SYMBOL",
  topGenes = 5,
  topGenesVar = "SYMBOL",
  facetNCol = 3
)

Interactive plot with feature annotation

The interactive scatter plot will be created by changing the typePlot parameter. An additional feature annotation can be shown when hovering over the points. The featuresVar parameter allows to specify the column names that should be used to annotate the features.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daScatterPlot(
  input = topTableList,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  featuresIdVar = "ENTREZID",
  featuresVar = c("SYMBOL", "GENENAME"),
  facetNCol = 3,
  typePlot = "interactive"
)

Add correlation

The Pearson correlation value can be shown in the plot when setting correlation parameter to TRUE.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

daScatterPlot(
  input = topTableList,
  coef = coefs[c(1, 2)],
  coefLabel = c("A", "B"),
  featuresIdVar = "ENTREZID",
  correlation = TRUE
)

Multiple coefficients

The function daWaterfallPlot() creates a barplot plot for the provided model or list of top tables. When more than one coefficient specified, multiple plots side by side are created.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")
features <- c(
  "24117", "381290", "226101", "78896", "231830", "16012", "16669", "55987",
  "231991", "14620", "20317", "74747", "11636", "20482", "194126", "270150",
  "17131", "16878", "20564", "73847" 
)

daWaterfallPlot(
  input = res.limma,
  coef = coefs,
  coefLabel = c("A", "B", "C", "D"),
  featuresIdVar = "ENTREZID",
  featuresVar = "SYMBOL",
  features = features,
  facetNCol = 4,
  colorVar = "adj.P.Val",
  color = c("maroon", "orange"),
  fillVar = "adj.P.Val",
  fill = c("maroon", "orange"),
  typePlot = "static"
)

Label top genes and genes of interest

The function daMAplot() creates a scatter plot (logFC versus mean expression) for the provided model or list of top tables. The top genes with highest absolute logFC and significance can be labeled. The number of top genes can be specified using the topGenes parameter. Top genes are labeled by feature names by default. It can be changed with topGenesVar. The genes of interest are indicated in green. Additionally, if coef indicates only one coefficient, the legend shows the number of significantly up- or down-regulated genes, or the number of non-significant genes. The points can be colored by direction (significant up- and down-regulated genes) if direction is set to TRUE. Customized colors can be used with color.

coefs <- c("B.LvsP", "L.LvsP")

daMAplot(
  input = res.limma, 
  coef = coefs[1], 
  coefLabel = "A",
  featuresIdVar = "ENTREZID",
  topGenes = 5,
  topGenesVar = "SYMBOL",
  genesToHighlight = genesOfInterest,
  genesToHighlightVar = "SYMBOL",
  direction = TRUE,
  color = c("steelblue", "firebrick", "grey")
)

Nested coefficient labels

The function daSignificantGenesBarplot() creates a barplot indicating the number of significant (up- and down-regulated) genes for the provided model or list of top tables. The coefficients can be grouped by specifying multiple sets of labels to the coefLabel parameter.

coefs <- c("B.LvsP", "L.LvsP", "B.PvsV", "L.PvsV")

coefsLabel <- list(
  sub("(.+)\\.(.+)", "\\2", coefs),
  sub("(.+)\\.(.+)", "\\1", coefs)
)

daSignificantGenesBarplot(
  input = res.limma, 
  coef = coefs, 
  coefLabel = coefsLabel,
  addPercentage = TRUE
)